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Img_Desc.py

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+# coding=utf-8
+# Copyright 2020 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Lint as: python3
+"""Image Description Dataset"""
+
+
+import json
+
+import datasets
+from datasets.tasks import QuestionAnsweringExtractive
+import pandas as pd
+
+
+logger = datasets.logging.get_logger(__name__)
+
+
+_CITATION = """\
+@article{2016arXiv160605250R,
+       author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
+                 Konstantin and {Liang}, Percy},
+        title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
+      journal = {arXiv e-prints},
+         year = 2016,
+          eid = {arXiv:1606.05250},
+        pages = {arXiv:1606.05250},
+archivePrefix = {arXiv},
+       eprint = {1606.05250},
+}
+"""
+
+_DESCRIPTION = """\
+Image descriptions for data science charts
+"""
+
+_URL = "https://huggingface.co/datasets/eduvedras/Img_Desc/resolve/main/images.tar.gz"
+
+class Image_DescriptionTargz(datasets.GeneratorBasedBuilder):
+
+    def _info(self):
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=datasets.Features(
+                {
+                    "Chart": datasets.Image(),
+                    "Description": datasets.Value("string"),
+                    "Chart_name": datasets.Value("string"),
+                }
+            ),
+            # No default supervised_keys (as we have to pass both question
+            # and context as input).
+            supervised_keys=None,
+            homepage="https://huggingface.co/datasets/eduvedras/Img_Desc",
+            citation=_CITATION,
+            task_templates=[
+                QuestionAnsweringExtractive(
+                    question_column="question", context_column="context", answers_column="answers"
+                )
+            ],
+        )
+
+    def _split_generators(self, dl_manager):
+        path = dl_manager.download(_URL)
+        image_iters = dl_manager.iter_archive(path)
+        metadata_train_path = "https://huggingface.co/datasets/eduvedras/Img_Desc/resolve/main/desc_dataset_train.csv" 
+        metadata_test_path = "https://huggingface.co/datasets/eduvedras/Img_Desc/resolve/main/desc_dataset_test.csv"
+
+        return [
+            datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"images": image_iters,
+                                                                           "metadata_path": metadata_train_path}),
+            datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"images": image_iters,
+                                                                           "metadata_path": metadata_test_path}),
+        ]
+
+    def _generate_examples(self, images, metadata_path):
+        metadata = pd.read_csv(metadata_path, sep=';')
+        idx = 0
+        for index, row in metadata.iterrows():
+            for filepath, image in images:
+                filepath = filepath.split('/')[-1]
+                if row['Chart'] in filepath:
+                    yield idx, {
+                        "Chart": {"path": filepath, "bytes": image.read()},
+                        "Description": row['description'],
+                        "Chart_name": row['Chart'],
+                    }
+                    break
+            idx += 1

desc_dataset.csv

@@ -0,0 +1,471 @@
+Chart;description
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable FAF and the second with variable Height.
+ObesityDataSet_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+ObesityDataSet_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+ObesityDataSet_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+ObesityDataSet_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+ObesityDataSet_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+ObesityDataSet_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+ObesityDataSet_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+ObesityDataSet_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
+ObesityDataSet_boxplots.png;A set of boxplots of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
+ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables ['CAEC', 'CALC', 'MTRANS', 'Gender', 'family_history_with_overweight', 'FAVC', 'SMOKE', 'SCC'].
+ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable NObeyesdad.
+ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+ObesityDataSet_histograms_numeric.png;A set of histograms of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Family_Size and the second with variable Work_Experience.
+customer_segmentation_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+customer_segmentation_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+customer_segmentation_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+customer_segmentation_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+customer_segmentation_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+customer_segmentation_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+customer_segmentation_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
+customer_segmentation_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'Work_Experience', 'Family_Size'].
+customer_segmentation_boxplots.png;A set of boxplots of the variables ['Age', 'Work_Experience', 'Family_Size'].
+customer_segmentation_histograms_symbolic.png;A set of bar charts of the variables ['Profession', 'Spending_Score', 'Var_1', 'Gender', 'Ever_Married', 'Graduated'].
+customer_segmentation_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Ever_Married', 'Graduated', 'Profession', 'Work_Experience', 'Family_Size', 'Var_1'].
+customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable Segmentation.
+customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+customer_segmentation_histograms_numeric.png;A set of histograms of the variables ['Age', 'Work_Experience', 'Family_Size'].
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable pH.
+urinalysis_tests_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+urinalysis_tests_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+urinalysis_tests_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+urinalysis_tests_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+urinalysis_tests_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+urinalysis_tests_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+urinalysis_tests_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
+urinalysis_tests_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'pH', 'Specific Gravity'].
+urinalysis_tests_boxplots.png;A set of boxplots of the variables ['Age', 'pH', 'Specific Gravity'].
+urinalysis_tests_histograms_symbolic.png;A set of bar charts of the variables ['Color', 'Transparency', 'Glucose', 'Protein', 'Epithelial Cells', 'Mucous Threads', 'Amorphous Urates', 'Bacteria', 'Gender'].
+urinalysis_tests_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Color'].
+urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable Diagnosis.
+urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+urinalysis_tests_histograms_numeric.png;A set of histograms of the variables ['Age', 'pH', 'Specific Gravity'].
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Ic and the second with variable Vb.
+detect_dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+detect_dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+detect_dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+detect_dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+detect_dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+detect_dataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+detect_dataset_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+detect_dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
+detect_dataset_boxplots.png;A set of boxplots of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
+detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable Output.
+detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+detect_dataset_histograms_numeric.png;A set of histograms of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable BMI and the second with variable Age.
+diabetes_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+diabetes_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+diabetes_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+diabetes_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+diabetes_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+diabetes_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+diabetes_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+diabetes_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
+diabetes_boxplots.png;A set of boxplots of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
+diabetes_class_histogram.png;A bar chart showing the distribution of the target variable Outcome.
+diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+diabetes_histograms_numeric.png;A set of histograms of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable ssc_p and the second with variable hsc_p.
+Placement_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Placement_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Placement_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Placement_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Placement_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Placement_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Placement_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
+Placement_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
+Placement_boxplots.png;A set of boxplots of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
+Placement_histograms_symbolic.png;A set of bar charts of the variables ['hsc_s', 'degree_t', 'gender', 'ssc_b', 'hsc_b', 'workex', 'specialisation'].
+Placement_class_histogram.png;A bar chart showing the distribution of the target variable status.
+Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Placement_histograms_numeric.png;A set of histograms of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Alkphos and the second with variable Sgot.
+Liver_Patient_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Liver_Patient_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Liver_Patient_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Liver_Patient_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Liver_Patient_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Liver_Patient_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Liver_Patient_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+Liver_Patient_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
+Liver_Patient_boxplots.png;A set of boxplots of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
+Liver_Patient_histograms_symbolic.png;A set of bar charts of the variables ['Gender'].
+Liver_Patient_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['AG_Ratio'].
+Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable Selector.
+Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Liver_Patient_histograms_numeric.png;A set of histograms of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable lead_time and the second with variable no_of_special_requests.
+Hotel_Reservations_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Hotel_Reservations_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Hotel_Reservations_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Hotel_Reservations_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Hotel_Reservations_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Hotel_Reservations_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Hotel_Reservations_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+Hotel_Reservations_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
+Hotel_Reservations_boxplots.png;A set of boxplots of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
+Hotel_Reservations_histograms_symbolic.png;A set of bar charts of the variables ['type_of_meal_plan', 'room_type_reserved', 'required_car_parking_space', 'arrival_year', 'repeated_guest'].
+Hotel_Reservations_class_histogram.png;A bar chart showing the distribution of the target variable booking_status.
+Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable basic_needs and the second with variable bullying.
+StressLevelDataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+StressLevelDataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+StressLevelDataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+StressLevelDataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+StressLevelDataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+StressLevelDataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+StressLevelDataset_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
+StressLevelDataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
+StressLevelDataset_boxplots.png;A set of boxplots of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
+StressLevelDataset_histograms_symbolic.png;A set of bar charts of the variables ['mental_health_history'].
+StressLevelDataset_class_histogram.png;A bar chart showing the distribution of the target variable stress_level.
+StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+StressLevelDataset_histograms_numeric.png;A set of histograms of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable density and the second with variable chlorides.
+WineQT_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+WineQT_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+WineQT_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+WineQT_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+WineQT_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+WineQT_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+WineQT_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
+WineQT_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
+WineQT_boxplots.png;A set of boxplots of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
+WineQT_class_histogram.png;A bar chart showing the distribution of the target variable quality.
+WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+WineQT_histograms_numeric.png;A set of histograms of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Loan_Amount_Term and the second with variable ApplicantIncome.
+loan_data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+loan_data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+loan_data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+loan_data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+loan_data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+loan_data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+loan_data_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+loan_data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
+loan_data_boxplots.png;A set of boxplots of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
+loan_data_histograms_symbolic.png;A set of bar charts of the variables ['Dependents', 'Property_Area', 'Gender', 'Married', 'Education', 'Self_Employed', 'Credit_History'].
+loan_data_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Gender', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History'].
+loan_data_class_histogram.png;A bar chart showing the distribution of the target variable Loan_Status.
+loan_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+loan_data_histograms_numeric.png;A set of histograms of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Area and the second with variable AspectRation.
+Dry_Bean_Dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Dry_Bean_Dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Dry_Bean_Dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Dry_Bean_Dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Dry_Bean_Dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Dry_Bean_Dataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Dry_Bean_Dataset_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+Dry_Bean_Dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
+Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
+Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable Class.
+Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable existing_credits and the second with variable residence_since.
+credit_customers_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+credit_customers_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+credit_customers_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+credit_customers_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+credit_customers_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+credit_customers_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+credit_customers_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+credit_customers_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
+credit_customers_boxplots.png;A set of boxplots of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
+credit_customers_histograms_symbolic.png;A set of bar charts of the variables ['checking_status', 'employment', 'other_parties', 'other_payment_plans', 'housing', 'num_dependents', 'own_telephone', 'foreign_worker'].
+credit_customers_class_histogram.png;A bar chart showing the distribution of the target variable class.
+credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+credit_customers_histograms_numeric.png;A set of histograms of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rainfall and the second with variable Pressure3pm.
+weatherAUS_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+weatherAUS_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+weatherAUS_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+weatherAUS_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+weatherAUS_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+weatherAUS_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+weatherAUS_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
+weatherAUS_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
+weatherAUS_boxplots.png;A set of boxplots of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
+weatherAUS_histograms_symbolic.png;A set of bar charts of the variables ['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday'].
+weatherAUS_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Rainfall', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm', 'RainToday'].
+weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable RainTomorrow.
+weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+weatherAUS_histograms_numeric.png;A set of histograms of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable displacement and the second with variable height.
+car_insurance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+car_insurance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+car_insurance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+car_insurance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+car_insurance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+car_insurance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+car_insurance_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+car_insurance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
+car_insurance_boxplots.png;A set of boxplots of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
+car_insurance_histograms_symbolic.png;A set of bar charts of the variables ['area_cluster', 'segment', 'model', 'fuel_type', 'max_torque', 'max_power', 'steering_type', 'is_esc', 'is_adjustable_steering'].
+car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable is_claim.
+car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+car_insurance_histograms_numeric.png;A set of histograms of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable slope and the second with variable restecg.
+heart_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+heart_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+heart_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+heart_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+heart_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+heart_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+heart_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
+heart_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
+heart_boxplots.png;A set of boxplots of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
+heart_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'fbs', 'exang'].
+heart_class_histogram.png;A bar chart showing the distribution of the target variable target.
+heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+heart_histograms_numeric.png;A set of histograms of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable perimeter_mean and the second with variable texture_worst.
+Breast_Cancer_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Breast_Cancer_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Breast_Cancer_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Breast_Cancer_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Breast_Cancer_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Breast_Cancer_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Breast_Cancer_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
+Breast_Cancer_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
+Breast_Cancer_boxplots.png;A set of boxplots of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
+Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable diagnosis.
+Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Breast_Cancer_histograms_numeric.png;A set of histograms of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Prior_purchases and the second with variable Customer_care_calls.
+e-commerce_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+e-commerce_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+e-commerce_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+e-commerce_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+e-commerce_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+e-commerce_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+e-commerce_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+e-commerce_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
+e-commerce_boxplots.png;A set of boxplots of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
+e-commerce_histograms_symbolic.png;A set of bar charts of the variables ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender'].
+e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable ReachedOnTime.
+e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+e-commerce_histograms_numeric.png;A set of histograms of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rotational speed [rpm] and the second with variable Torque [Nm].
+maintenance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+maintenance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+maintenance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+maintenance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+maintenance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+maintenance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+maintenance_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
+maintenance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
+maintenance_boxplots.png;A set of boxplots of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
+maintenance_histograms_symbolic.png;A set of bar charts of the variables ['Type', 'TWF', 'HDF', 'PWF', 'OSF', 'RNF'].
+maintenance_class_histogram.png;A bar chart showing the distribution of the target variable Machine_failure.
+maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+maintenance_histograms_numeric.png;A set of histograms of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable NumOfProducts.
+Churn_Modelling_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Churn_Modelling_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Churn_Modelling_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Churn_Modelling_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Churn_Modelling_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Churn_Modelling_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Churn_Modelling_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+Churn_Modelling_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
+Churn_Modelling_boxplots.png;A set of boxplots of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
+Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables ['Geography', 'Gender', 'HasCrCard', 'IsActiveMember'].
+Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable Exited.
+Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Churn_Modelling_histograms_numeric.png;A set of histograms of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable MAJORSKEWNESS and the second with variable CIRCULARITY.
+vehicle_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+vehicle_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+vehicle_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+vehicle_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+vehicle_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+vehicle_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+vehicle_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
+vehicle_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
+vehicle_boxplots.png;A set of boxplots of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
+vehicle_class_histogram.png;A bar chart showing the distribution of the target variable target.
+vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+vehicle_histograms_numeric.png;A set of histograms of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable hours-per-week and the second with variable capital-loss.
+adult_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+adult_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+adult_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+adult_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+adult_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+adult_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+adult_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+adult_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
+adult_boxplots.png;A set of boxplots of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
+adult_histograms_symbolic.png;A set of bar charts of the variables ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender'].
+adult_class_histogram.png;A bar chart showing the distribution of the target variable income.
+adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+adult_histograms_numeric.png;A set of histograms of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable CARDIOVASCULAR and the second with variable ASHTMA.
+Covid_Data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Covid_Data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Covid_Data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Covid_Data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Covid_Data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Covid_Data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Covid_Data_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
+Covid_Data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
+Covid_Data_boxplots.png;A set of boxplots of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
+Covid_Data_histograms_symbolic.png;A set of bar charts of the variables ['USMER', 'SEX', 'PATIENT_TYPE'].
+Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable CLASSIFICATION.
+Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Covid_Data_histograms_numeric.png;A set of histograms of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable dec and the second with variable mjd.
+sky_survey_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+sky_survey_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+sky_survey_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+sky_survey_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+sky_survey_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+sky_survey_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+sky_survey_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+sky_survey_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
+sky_survey_boxplots.png;A set of boxplots of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
+sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable class.
+sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+sky_survey_histograms_numeric.png;A set of histograms of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Total phenols and the second with variable Proanthocyanins.
+Wine_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Wine_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Wine_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Wine_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Wine_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Wine_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Wine_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
+Wine_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
+Wine_boxplots.png;A set of boxplots of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
+Wine_class_histogram.png;A bar chart showing the distribution of the target variable Class.
+Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Wine_histograms_numeric.png;A set of histograms of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Hardness and the second with variable Chloramines.
+water_potability_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+water_potability_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+water_potability_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+water_potability_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+water_potability_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+water_potability_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+water_potability_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
+water_potability_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
+water_potability_boxplots.png;A set of boxplots of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
+water_potability_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['ph', 'Sulfate', 'Trihalomethanes'].
+water_potability_class_histogram.png;A bar chart showing the distribution of the target variable Potability.
+water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+water_potability_histograms_numeric.png;A set of histograms of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Height and the second with variable Diameter.
+abalone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+abalone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+abalone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+abalone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+abalone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+abalone_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+abalone_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+abalone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
+abalone_boxplots.png;A set of boxplots of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
+abalone_class_histogram.png;A bar chart showing the distribution of the target variable Sex.
+abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+abalone_histograms_numeric.png;A set of histograms of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable SMK_stat_type_cd and the second with variable gamma_GTP.
+smoking_drinking_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+smoking_drinking_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+smoking_drinking_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+smoking_drinking_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+smoking_drinking_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+smoking_drinking_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+smoking_drinking_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
+smoking_drinking_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
+smoking_drinking_boxplots.png;A set of boxplots of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
+smoking_drinking_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'hear_left', 'hear_right'].
+smoking_drinking_class_histogram.png;A bar chart showing the distribution of the target variable DRK_YN.
+smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+smoking_drinking_histograms_numeric.png;A set of histograms of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable skewness and the second with variable curtosis.
+BankNoteAuthentication_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+BankNoteAuthentication_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+BankNoteAuthentication_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+BankNoteAuthentication_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+BankNoteAuthentication_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+BankNoteAuthentication_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+BankNoteAuthentication_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+BankNoteAuthentication_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['variance', 'skewness', 'curtosis', 'entropy'].
+BankNoteAuthentication_boxplots.png;A set of boxplots of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
+BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable class.
+BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
+Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first and second decisions are made with variable PetalWidthCm.
+Iris_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Iris_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Iris_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Iris_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Iris_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Iris_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Iris_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+Iris_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
+Iris_boxplots.png;A set of boxplots of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
+Iris_class_histogram.png;A bar chart showing the distribution of the target variable Species.
+Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Iris_histograms_numeric.png;A set of histograms of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable int_memory and the second with variable mobile_wt.
+phone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+phone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+phone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+phone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+phone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+phone_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+phone_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
+phone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
+phone_boxplots.png;A set of boxplots of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
+phone_histograms_symbolic.png;A set of bar charts of the variables ['blue', 'dual_sim', 'four_g', 'three_g', 'touch_screen', 'wifi'].
+phone_class_histogram.png;A bar chart showing the distribution of the target variable price_range.
+phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+phone_histograms_numeric.png;A set of histograms of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Pclass and the second with variable Parch.
+Titanic_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Titanic_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Titanic_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Titanic_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Titanic_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Titanic_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Titanic_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
+Titanic_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
+Titanic_boxplots.png;A set of boxplots of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
+Titanic_histograms_symbolic.png;A set of bar charts of the variables ['Embarked', 'Sex'].
+Titanic_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Age', 'Embarked'].
+Titanic_class_histogram.png;A bar chart showing the distribution of the target variable Survived.
+Titanic_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Titanic_histograms_numeric.png;A set of histograms of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Juiciness and the second with variable Crunchiness.
+apple_quality_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+apple_quality_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+apple_quality_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+apple_quality_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+apple_quality_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+apple_quality_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+apple_quality_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
+apple_quality_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
+apple_quality_boxplots.png;A set of boxplots of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
+apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable Quality.
+apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+apple_quality_histograms_numeric.png;A set of histograms of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable JoiningYear and the second with variable ExperienceInCurrentDomain.
+Employee_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Employee_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Employee_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Employee_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Employee_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Employee_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Employee_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+Employee_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].
+Employee_boxplots.png;A set of boxplots of the variables ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].
+Employee_histograms_symbolic.png;A set of bar charts of the variables ['Education', 'City', 'Gender', 'EverBenched'].
+Employee_class_histogram.png;A bar chart showing the distribution of the target variable LeaveOrNot.
+Employee_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Employee_histograms_numeric.png;A set of histograms of the variables ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].

desc_dataset_test.csv

@@ -0,0 +1,16 @@
+Chart;description
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Pclass and the second with variable Parch.
+Titanic_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Titanic_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Titanic_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Titanic_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Titanic_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Titanic_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Titanic_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
+Titanic_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
+Titanic_boxplots.png;A set of boxplots of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
+Titanic_histograms_symbolic.png;A set of bar charts of the variables ['Embarked', 'Sex'].
+Titanic_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Age', 'Embarked'].
+Titanic_class_histogram.png;A bar chart showing the distribution of the target variable Survived.
+Titanic_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Titanic_histograms_numeric.png;A set of histograms of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].

desc_dataset_train.csv

@@ -0,0 +1,456 @@
+Chart;description
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable FAF and the second with variable Height.
+ObesityDataSet_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+ObesityDataSet_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+ObesityDataSet_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+ObesityDataSet_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+ObesityDataSet_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+ObesityDataSet_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+ObesityDataSet_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+ObesityDataSet_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
+ObesityDataSet_boxplots.png;A set of boxplots of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
+ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables ['CAEC', 'CALC', 'MTRANS', 'Gender', 'family_history_with_overweight', 'FAVC', 'SMOKE', 'SCC'].
+ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable NObeyesdad.
+ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+ObesityDataSet_histograms_numeric.png;A set of histograms of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Family_Size and the second with variable Work_Experience.
+customer_segmentation_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+customer_segmentation_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+customer_segmentation_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+customer_segmentation_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+customer_segmentation_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+customer_segmentation_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+customer_segmentation_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
+customer_segmentation_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'Work_Experience', 'Family_Size'].
+customer_segmentation_boxplots.png;A set of boxplots of the variables ['Age', 'Work_Experience', 'Family_Size'].
+customer_segmentation_histograms_symbolic.png;A set of bar charts of the variables ['Profession', 'Spending_Score', 'Var_1', 'Gender', 'Ever_Married', 'Graduated'].
+customer_segmentation_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Ever_Married', 'Graduated', 'Profession', 'Work_Experience', 'Family_Size', 'Var_1'].
+customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable Segmentation.
+customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+customer_segmentation_histograms_numeric.png;A set of histograms of the variables ['Age', 'Work_Experience', 'Family_Size'].
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable pH.
+urinalysis_tests_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+urinalysis_tests_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+urinalysis_tests_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+urinalysis_tests_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+urinalysis_tests_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+urinalysis_tests_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+urinalysis_tests_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
+urinalysis_tests_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'pH', 'Specific Gravity'].
+urinalysis_tests_boxplots.png;A set of boxplots of the variables ['Age', 'pH', 'Specific Gravity'].
+urinalysis_tests_histograms_symbolic.png;A set of bar charts of the variables ['Color', 'Transparency', 'Glucose', 'Protein', 'Epithelial Cells', 'Mucous Threads', 'Amorphous Urates', 'Bacteria', 'Gender'].
+urinalysis_tests_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Color'].
+urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable Diagnosis.
+urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+urinalysis_tests_histograms_numeric.png;A set of histograms of the variables ['Age', 'pH', 'Specific Gravity'].
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Ic and the second with variable Vb.
+detect_dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+detect_dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+detect_dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+detect_dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+detect_dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+detect_dataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+detect_dataset_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+detect_dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
+detect_dataset_boxplots.png;A set of boxplots of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
+detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable Output.
+detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+detect_dataset_histograms_numeric.png;A set of histograms of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable BMI and the second with variable Age.
+diabetes_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+diabetes_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+diabetes_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+diabetes_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+diabetes_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+diabetes_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+diabetes_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+diabetes_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
+diabetes_boxplots.png;A set of boxplots of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
+diabetes_class_histogram.png;A bar chart showing the distribution of the target variable Outcome.
+diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+diabetes_histograms_numeric.png;A set of histograms of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable ssc_p and the second with variable hsc_p.
+Placement_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Placement_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Placement_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Placement_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Placement_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Placement_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Placement_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
+Placement_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
+Placement_boxplots.png;A set of boxplots of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
+Placement_histograms_symbolic.png;A set of bar charts of the variables ['hsc_s', 'degree_t', 'gender', 'ssc_b', 'hsc_b', 'workex', 'specialisation'].
+Placement_class_histogram.png;A bar chart showing the distribution of the target variable status.
+Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Placement_histograms_numeric.png;A set of histograms of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Alkphos and the second with variable Sgot.
+Liver_Patient_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Liver_Patient_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Liver_Patient_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Liver_Patient_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Liver_Patient_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Liver_Patient_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Liver_Patient_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+Liver_Patient_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
+Liver_Patient_boxplots.png;A set of boxplots of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
+Liver_Patient_histograms_symbolic.png;A set of bar charts of the variables ['Gender'].
+Liver_Patient_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['AG_Ratio'].
+Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable Selector.
+Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Liver_Patient_histograms_numeric.png;A set of histograms of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable lead_time and the second with variable no_of_special_requests.
+Hotel_Reservations_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Hotel_Reservations_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Hotel_Reservations_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Hotel_Reservations_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Hotel_Reservations_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Hotel_Reservations_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Hotel_Reservations_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+Hotel_Reservations_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
+Hotel_Reservations_boxplots.png;A set of boxplots of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
+Hotel_Reservations_histograms_symbolic.png;A set of bar charts of the variables ['type_of_meal_plan', 'room_type_reserved', 'required_car_parking_space', 'arrival_year', 'repeated_guest'].
+Hotel_Reservations_class_histogram.png;A bar chart showing the distribution of the target variable booking_status.
+Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable basic_needs and the second with variable bullying.
+StressLevelDataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+StressLevelDataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+StressLevelDataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+StressLevelDataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+StressLevelDataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+StressLevelDataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+StressLevelDataset_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
+StressLevelDataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
+StressLevelDataset_boxplots.png;A set of boxplots of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
+StressLevelDataset_histograms_symbolic.png;A set of bar charts of the variables ['mental_health_history'].
+StressLevelDataset_class_histogram.png;A bar chart showing the distribution of the target variable stress_level.
+StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+StressLevelDataset_histograms_numeric.png;A set of histograms of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable density and the second with variable chlorides.
+WineQT_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+WineQT_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+WineQT_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+WineQT_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+WineQT_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+WineQT_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+WineQT_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
+WineQT_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
+WineQT_boxplots.png;A set of boxplots of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
+WineQT_class_histogram.png;A bar chart showing the distribution of the target variable quality.
+WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+WineQT_histograms_numeric.png;A set of histograms of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Loan_Amount_Term and the second with variable ApplicantIncome.
+loan_data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+loan_data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+loan_data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+loan_data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+loan_data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+loan_data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+loan_data_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+loan_data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
+loan_data_boxplots.png;A set of boxplots of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
+loan_data_histograms_symbolic.png;A set of bar charts of the variables ['Dependents', 'Property_Area', 'Gender', 'Married', 'Education', 'Self_Employed', 'Credit_History'].
+loan_data_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Gender', 'Dependents', 'Self_Employed', 'Loan_Amount_Term', 'Credit_History'].
+loan_data_class_histogram.png;A bar chart showing the distribution of the target variable Loan_Status.
+loan_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+loan_data_histograms_numeric.png;A set of histograms of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Area and the second with variable AspectRation.
+Dry_Bean_Dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Dry_Bean_Dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Dry_Bean_Dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Dry_Bean_Dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Dry_Bean_Dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Dry_Bean_Dataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Dry_Bean_Dataset_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+Dry_Bean_Dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
+Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
+Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable Class.
+Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable existing_credits and the second with variable residence_since.
+credit_customers_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+credit_customers_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+credit_customers_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+credit_customers_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+credit_customers_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+credit_customers_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+credit_customers_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+credit_customers_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
+credit_customers_boxplots.png;A set of boxplots of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
+credit_customers_histograms_symbolic.png;A set of bar charts of the variables ['checking_status', 'employment', 'other_parties', 'other_payment_plans', 'housing', 'num_dependents', 'own_telephone', 'foreign_worker'].
+credit_customers_class_histogram.png;A bar chart showing the distribution of the target variable class.
+credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+credit_customers_histograms_numeric.png;A set of histograms of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rainfall and the second with variable Pressure3pm.
+weatherAUS_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+weatherAUS_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+weatherAUS_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+weatherAUS_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+weatherAUS_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+weatherAUS_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+weatherAUS_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
+weatherAUS_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
+weatherAUS_boxplots.png;A set of boxplots of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
+weatherAUS_histograms_symbolic.png;A set of bar charts of the variables ['Location', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'RainToday'].
+weatherAUS_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['Rainfall', 'WindGustDir', 'WindDir9am', 'WindDir3pm', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm', 'RainToday'].
+weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable RainTomorrow.
+weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+weatherAUS_histograms_numeric.png;A set of histograms of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable displacement and the second with variable height.
+car_insurance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+car_insurance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+car_insurance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+car_insurance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+car_insurance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+car_insurance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+car_insurance_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
+car_insurance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
+car_insurance_boxplots.png;A set of boxplots of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
+car_insurance_histograms_symbolic.png;A set of bar charts of the variables ['area_cluster', 'segment', 'model', 'fuel_type', 'max_torque', 'max_power', 'steering_type', 'is_esc', 'is_adjustable_steering'].
+car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable is_claim.
+car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+car_insurance_histograms_numeric.png;A set of histograms of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable slope and the second with variable restecg.
+heart_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+heart_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+heart_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+heart_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+heart_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+heart_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+heart_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
+heart_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
+heart_boxplots.png;A set of boxplots of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
+heart_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'fbs', 'exang'].
+heart_class_histogram.png;A bar chart showing the distribution of the target variable target.
+heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+heart_histograms_numeric.png;A set of histograms of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable perimeter_mean and the second with variable texture_worst.
+Breast_Cancer_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Breast_Cancer_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Breast_Cancer_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Breast_Cancer_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Breast_Cancer_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Breast_Cancer_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Breast_Cancer_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
+Breast_Cancer_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
+Breast_Cancer_boxplots.png;A set of boxplots of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
+Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable diagnosis.
+Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Breast_Cancer_histograms_numeric.png;A set of histograms of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Prior_purchases and the second with variable Customer_care_calls.
+e-commerce_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+e-commerce_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+e-commerce_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+e-commerce_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+e-commerce_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+e-commerce_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+e-commerce_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+e-commerce_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
+e-commerce_boxplots.png;A set of boxplots of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
+e-commerce_histograms_symbolic.png;A set of bar charts of the variables ['Warehouse_block', 'Mode_of_Shipment', 'Product_importance', 'Gender'].
+e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable ReachedOnTime.
+e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+e-commerce_histograms_numeric.png;A set of histograms of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rotational speed [rpm] and the second with variable Torque [Nm].
+maintenance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+maintenance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+maintenance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+maintenance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+maintenance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+maintenance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+maintenance_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
+maintenance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
+maintenance_boxplots.png;A set of boxplots of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
+maintenance_histograms_symbolic.png;A set of bar charts of the variables ['Type', 'TWF', 'HDF', 'PWF', 'OSF', 'RNF'].
+maintenance_class_histogram.png;A bar chart showing the distribution of the target variable Machine_failure.
+maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+maintenance_histograms_numeric.png;A set of histograms of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable NumOfProducts.
+Churn_Modelling_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Churn_Modelling_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Churn_Modelling_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Churn_Modelling_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Churn_Modelling_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Churn_Modelling_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Churn_Modelling_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+Churn_Modelling_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
+Churn_Modelling_boxplots.png;A set of boxplots of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
+Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables ['Geography', 'Gender', 'HasCrCard', 'IsActiveMember'].
+Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable Exited.
+Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Churn_Modelling_histograms_numeric.png;A set of histograms of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable MAJORSKEWNESS and the second with variable CIRCULARITY.
+vehicle_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+vehicle_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+vehicle_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+vehicle_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+vehicle_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+vehicle_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+vehicle_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
+vehicle_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
+vehicle_boxplots.png;A set of boxplots of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
+vehicle_class_histogram.png;A bar chart showing the distribution of the target variable target.
+vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+vehicle_histograms_numeric.png;A set of histograms of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable hours-per-week and the second with variable capital-loss.
+adult_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+adult_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+adult_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+adult_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+adult_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+adult_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+adult_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
+adult_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
+adult_boxplots.png;A set of boxplots of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
+adult_histograms_symbolic.png;A set of bar charts of the variables ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'gender'].
+adult_class_histogram.png;A bar chart showing the distribution of the target variable income.
+adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+adult_histograms_numeric.png;A set of histograms of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable CARDIOVASCULAR and the second with variable ASHTMA.
+Covid_Data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Covid_Data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Covid_Data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Covid_Data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Covid_Data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Covid_Data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Covid_Data_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
+Covid_Data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
+Covid_Data_boxplots.png;A set of boxplots of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
+Covid_Data_histograms_symbolic.png;A set of bar charts of the variables ['USMER', 'SEX', 'PATIENT_TYPE'].
+Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable CLASSIFICATION.
+Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Covid_Data_histograms_numeric.png;A set of histograms of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable dec and the second with variable mjd.
+sky_survey_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+sky_survey_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+sky_survey_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+sky_survey_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+sky_survey_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+sky_survey_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+sky_survey_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+sky_survey_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
+sky_survey_boxplots.png;A set of boxplots of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
+sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable class.
+sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+sky_survey_histograms_numeric.png;A set of histograms of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Total phenols and the second with variable Proanthocyanins.
+Wine_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Wine_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Wine_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Wine_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Wine_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Wine_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Wine_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
+Wine_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
+Wine_boxplots.png;A set of boxplots of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
+Wine_class_histogram.png;A bar chart showing the distribution of the target variable Class.
+Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Wine_histograms_numeric.png;A set of histograms of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Hardness and the second with variable Chloramines.
+water_potability_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+water_potability_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+water_potability_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+water_potability_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+water_potability_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+water_potability_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+water_potability_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
+water_potability_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
+water_potability_boxplots.png;A set of boxplots of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
+water_potability_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: ['ph', 'Sulfate', 'Trihalomethanes'].
+water_potability_class_histogram.png;A bar chart showing the distribution of the target variable Potability.
+water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+water_potability_histograms_numeric.png;A set of histograms of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Height and the second with variable Diameter.
+abalone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+abalone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+abalone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+abalone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+abalone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+abalone_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+abalone_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
+abalone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
+abalone_boxplots.png;A set of boxplots of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
+abalone_class_histogram.png;A bar chart showing the distribution of the target variable Sex.
+abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+abalone_histograms_numeric.png;A set of histograms of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable SMK_stat_type_cd and the second with variable gamma_GTP.
+smoking_drinking_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+smoking_drinking_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+smoking_drinking_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+smoking_drinking_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+smoking_drinking_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+smoking_drinking_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+smoking_drinking_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
+smoking_drinking_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
+smoking_drinking_boxplots.png;A set of boxplots of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
+smoking_drinking_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'hear_left', 'hear_right'].
+smoking_drinking_class_histogram.png;A bar chart showing the distribution of the target variable DRK_YN.
+smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+smoking_drinking_histograms_numeric.png;A set of histograms of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable skewness and the second with variable curtosis.
+BankNoteAuthentication_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+BankNoteAuthentication_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+BankNoteAuthentication_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+BankNoteAuthentication_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+BankNoteAuthentication_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+BankNoteAuthentication_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+BankNoteAuthentication_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+BankNoteAuthentication_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['variance', 'skewness', 'curtosis', 'entropy'].
+BankNoteAuthentication_boxplots.png;A set of boxplots of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
+BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable class.
+BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
+Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first and second decisions are made with variable PetalWidthCm.
+Iris_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Iris_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Iris_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Iris_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Iris_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Iris_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Iris_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+Iris_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
+Iris_boxplots.png;A set of boxplots of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
+Iris_class_histogram.png;A bar chart showing the distribution of the target variable Species.
+Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Iris_histograms_numeric.png;A set of histograms of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable int_memory and the second with variable mobile_wt.
+phone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+phone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+phone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+phone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+phone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+phone_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+phone_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
+phone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
+phone_boxplots.png;A set of boxplots of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
+phone_histograms_symbolic.png;A set of bar charts of the variables ['blue', 'dual_sim', 'four_g', 'three_g', 'touch_screen', 'wifi'].
+phone_class_histogram.png;A bar chart showing the distribution of the target variable price_range.
+phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+phone_histograms_numeric.png;A set of histograms of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Juiciness and the second with variable Crunchiness.
+apple_quality_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+apple_quality_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+apple_quality_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+apple_quality_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+apple_quality_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+apple_quality_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+apple_quality_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
+apple_quality_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
+apple_quality_boxplots.png;A set of boxplots of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
+apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable Quality.
+apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+apple_quality_histograms_numeric.png;A set of histograms of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable JoiningYear and the second with variable ExperienceInCurrentDomain.
+Employee_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
+Employee_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Employee_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
+Employee_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
+Employee_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
+Employee_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
+Employee_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
+Employee_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].
+Employee_boxplots.png;A set of boxplots of the variables ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].
+Employee_histograms_symbolic.png;A set of bar charts of the variables ['Education', 'City', 'Gender', 'EverBenched'].
+Employee_class_histogram.png;A bar chart showing the distribution of the target variable LeaveOrNot.
+Employee_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
+Employee_histograms_numeric.png;A set of histograms of the variables ['JoiningYear', 'PaymentTier', 'Age', 'ExperienceInCurrentDomain'].

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