New dataset

Img_Desc_Templates.py

@@ -0,0 +1,100 @@
+# 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_Templates/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_Templates",
+            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_Templates/resolve/main/desc_dataset_train.csv" 
+        metadata_test_path = "https://huggingface.co/datasets/eduvedras/Img_Desc_Templates/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 +1,460 @@
+Chart;description
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+ObesityDataSet_boxplots.png;A set of boxplots of the variables [].
+ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables [].
+ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+customer_segmentation_boxplots.png;A set of boxplots of the variables [].
+customer_segmentation_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+urinalysis_tests_boxplots.png;A set of boxplots of the variables [].
+urinalysis_tests_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+detect_dataset_boxplots.png;A set of boxplots of the variables [].
+detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+diabetes_boxplots.png;A set of boxplots of the variables [].
+diabetes_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Placement_boxplots.png;A set of boxplots of the variables [].
+Placement_histograms_symbolic.png;A set of bar charts of the variables [].
+Placement_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Liver_Patient_boxplots.png;A set of boxplots of the variables [].
+Liver_Patient_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Hotel_Reservations_boxplots.png;A set of boxplots of the variables [].
+Hotel_Reservations_histograms_symbolic.png;A set of bar charts of the variables [].
+Hotel_Reservations_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+StressLevelDataset_boxplots.png;A set of boxplots of the variables [].
+StressLevelDataset_histograms_symbolic.png;A set of bar charts of the variables [].
+StressLevelDataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+WineQT_boxplots.png;A set of boxplots of the variables [].
+WineQT_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+loan_data_boxplots.png;A set of boxplots of the variables [].
+loan_data_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+loan_data_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables [].
+Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+credit_customers_boxplots.png;A set of boxplots of the variables [].
+credit_customers_histograms_symbolic.png;A set of bar charts of the variables [].
+credit_customers_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+weatherAUS_boxplots.png;A set of boxplots of the variables [].
+weatherAUS_histograms_symbolic.png;A set of bar charts of the variables [].
+weatherAUS_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
+weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+car_insurance_boxplots.png;A set of boxplots of the variables [].
+car_insurance_histograms_symbolic.png;A set of bar charts of the variables [].
+car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+heart_boxplots.png;A set of boxplots of the variables [].
+heart_histograms_symbolic.png;A set of bar charts of the variables [].
+heart_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Breast_Cancer_boxplots.png;A set of boxplots of the variables [].
+Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+e-commerce_boxplots.png;A set of boxplots of the variables [].
+e-commerce_histograms_symbolic.png;A set of bar charts of the variables [].
+e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+maintenance_boxplots.png;A set of boxplots of the variables [].
+maintenance_histograms_symbolic.png;A set of bar charts of the variables [].
+maintenance_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Churn_Modelling_boxplots.png;A set of boxplots of the variables [].
+Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables [].
+Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+vehicle_boxplots.png;A set of boxplots of the variables [].
+vehicle_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+adult_boxplots.png;A set of boxplots of the variables [].
+adult_histograms_symbolic.png;A set of bar charts of the variables [].
+adult_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Covid_Data_boxplots.png;A set of boxplots of the variables [].
+Covid_Data_histograms_symbolic.png;A set of bar charts of the variables [].
+Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+sky_survey_boxplots.png;A set of boxplots of the variables [].
+sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+Wine_boxplots.png;A set of boxplots of the variables [].
+Wine_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+water_potability_boxplots.png;A set of boxplots of the variables [].
+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: [].
+water_potability_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+abalone_boxplots.png;A set of boxplots of the variables [].
+abalone_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+smoking_drinking_boxplots.png;A set of boxplots of the variables [].
+smoking_drinking_histograms_symbolic.png;A set of bar charts of the variables [].
+smoking_drinking_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+BankNoteAuthentication_boxplots.png;A set of boxplots of the variables [].
+BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+Iris_boxplots.png;A set of boxplots of the variables [].
+Iris_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+phone_boxplots.png;A set of boxplots of the variables [].
+phone_histograms_symbolic.png;A set of bar charts of the variables [].
+phone_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Titanic_boxplots.png;A set of boxplots of the variables [].
+Titanic_histograms_symbolic.png;A set of bar charts of the variables [].
+Titanic_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
+Titanic_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+apple_quality_boxplots.png;A set of boxplots of the variables [].
+apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Employee_boxplots.png;A set of boxplots of the variables [].
+Employee_histograms_symbolic.png;A set of bar charts of the variables [].
+Employee_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].

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 [] and the second with variable [].
+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 [].
+Titanic_boxplots.png;A set of boxplots of the variables [].
+Titanic_histograms_symbolic.png;A set of bar charts of the variables [].
+Titanic_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
+Titanic_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].

desc_dataset_train.csv

@@ -0,0 +1,445 @@
+Chart;description
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+ObesityDataSet_boxplots.png;A set of boxplots of the variables [].
+ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables [].
+ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+customer_segmentation_boxplots.png;A set of boxplots of the variables [].
+customer_segmentation_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+urinalysis_tests_boxplots.png;A set of boxplots of the variables [].
+urinalysis_tests_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+detect_dataset_boxplots.png;A set of boxplots of the variables [].
+detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+diabetes_boxplots.png;A set of boxplots of the variables [].
+diabetes_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Placement_boxplots.png;A set of boxplots of the variables [].
+Placement_histograms_symbolic.png;A set of bar charts of the variables [].
+Placement_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Liver_Patient_boxplots.png;A set of boxplots of the variables [].
+Liver_Patient_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Hotel_Reservations_boxplots.png;A set of boxplots of the variables [].
+Hotel_Reservations_histograms_symbolic.png;A set of bar charts of the variables [].
+Hotel_Reservations_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+StressLevelDataset_boxplots.png;A set of boxplots of the variables [].
+StressLevelDataset_histograms_symbolic.png;A set of bar charts of the variables [].
+StressLevelDataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+WineQT_boxplots.png;A set of boxplots of the variables [].
+WineQT_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+loan_data_boxplots.png;A set of boxplots of the variables [].
+loan_data_histograms_symbolic.png;A set of bar charts of the variables [].
+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: [].
+loan_data_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables [].
+Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+credit_customers_boxplots.png;A set of boxplots of the variables [].
+credit_customers_histograms_symbolic.png;A set of bar charts of the variables [].
+credit_customers_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+weatherAUS_boxplots.png;A set of boxplots of the variables [].
+weatherAUS_histograms_symbolic.png;A set of bar charts of the variables [].
+weatherAUS_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
+weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+car_insurance_boxplots.png;A set of boxplots of the variables [].
+car_insurance_histograms_symbolic.png;A set of bar charts of the variables [].
+car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+heart_boxplots.png;A set of boxplots of the variables [].
+heart_histograms_symbolic.png;A set of bar charts of the variables [].
+heart_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Breast_Cancer_boxplots.png;A set of boxplots of the variables [].
+Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+e-commerce_boxplots.png;A set of boxplots of the variables [].
+e-commerce_histograms_symbolic.png;A set of bar charts of the variables [].
+e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+maintenance_boxplots.png;A set of boxplots of the variables [].
+maintenance_histograms_symbolic.png;A set of bar charts of the variables [].
+maintenance_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Churn_Modelling_boxplots.png;A set of boxplots of the variables [].
+Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables [].
+Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+vehicle_boxplots.png;A set of boxplots of the variables [].
+vehicle_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+adult_boxplots.png;A set of boxplots of the variables [].
+adult_histograms_symbolic.png;A set of bar charts of the variables [].
+adult_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Covid_Data_boxplots.png;A set of boxplots of the variables [].
+Covid_Data_histograms_symbolic.png;A set of bar charts of the variables [].
+Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+sky_survey_boxplots.png;A set of boxplots of the variables [].
+sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+Wine_boxplots.png;A set of boxplots of the variables [].
+Wine_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+water_potability_boxplots.png;A set of boxplots of the variables [].
+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: [].
+water_potability_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+abalone_boxplots.png;A set of boxplots of the variables [].
+abalone_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+smoking_drinking_boxplots.png;A set of boxplots of the variables [].
+smoking_drinking_histograms_symbolic.png;A set of bar charts of the variables [].
+smoking_drinking_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+BankNoteAuthentication_boxplots.png;A set of boxplots of the variables [].
+BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+Iris_boxplots.png;A set of boxplots of the variables [].
+Iris_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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_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 [].
+phone_boxplots.png;A set of boxplots of the variables [].
+phone_histograms_symbolic.png;A set of bar charts of the variables [].
+phone_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+apple_quality_boxplots.png;A set of boxplots of the variables [].
+apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+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 [].
+Employee_boxplots.png;A set of boxplots of the variables [].
+Employee_histograms_symbolic.png;A set of bar charts of the variables [].
+Employee_class_histogram.png;A bar chart showing the distribution of the target variable [].
+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 [].

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