condition

Img_Desc.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 
 # Lint as: python3
-"""Image Description Dataset."""
+"""Image Description Dataset"""
 
 
 import json

desc_dataset.csv

@@ -1,5 +1,5 @@
 Chart;description
-ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable FAF and the second with variable Height.
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition FAF <= 2.0 and the second with the condition Height <= 1.72.
 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.
@@ -12,7 +12,7 @@ ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables ['CA
 ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable NObeyesdad.
 ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 ObesityDataSet_histograms_numeric.png;A set of histograms of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
-customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Family_Size and the second with variable Work_Experience.
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Family_Size <= 2.5 and the second with the condition Work_Experience <= 9.5.
 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.
@@ -26,7 +26,7 @@ customer_segmentation_mv.png;A bar chart showing the number of missing values pe
 customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable Segmentation.
 customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 customer_segmentation_histograms_numeric.png;A set of histograms of the variables ['Age', 'Work_Experience', 'Family_Size'].
-urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable pH.
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Age <= 0.1 and the second with the condition pH <= 5.5.
 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.
@@ -41,7 +41,7 @@ urinalysis_tests_mv.png;A bar chart showing the number of missing values per var
 urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable Diagnosis.
 urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 urinalysis_tests_histograms_numeric.png;A set of histograms of the variables ['Age', 'pH', 'Specific Gravity'].
-detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Ic and the second with variable Vb.
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Ic <= 71.01 and the second with the condition Vb <= -0.37.
 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.
@@ -54,7 +54,7 @@ detect_dataset_boxplots.png;A set of boxplots of the variables ['Ia', 'Ib', 'Ic'
 detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable Output.
 detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 detect_dataset_histograms_numeric.png;A set of histograms of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
-diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable BMI and the second with variable Age.
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition BMI <= 29.85 and the second with the condition Age <= 27.5.
 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.
@@ -67,7 +67,7 @@ diabetes_boxplots.png;A set of boxplots of the variables ['Pregnancies', 'Glucos
 diabetes_class_histogram.png;A bar chart showing the distribution of the target variable Outcome.
 diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 diabetes_histograms_numeric.png;A set of histograms of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
-Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable ssc_p and the second with variable hsc_p.
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition ssc_p <= 60.09 and the second with the condition hsc_p <= 70.24.
 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.
@@ -81,7 +81,7 @@ Placement_histograms_symbolic.png;A set of bar charts of the variables ['hsc_s',
 Placement_class_histogram.png;A bar chart showing the distribution of the target variable status.
 Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Placement_histograms_numeric.png;A set of histograms of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
-Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Alkphos and the second with variable Sgot.
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Alkphos <= 211.5 and the second with the condition Sgot <= 26.5.
 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.
@@ -96,7 +96,7 @@ Liver_Patient_mv.png;A bar chart showing the number of missing values per variab
 Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable Selector.
 Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Liver_Patient_histograms_numeric.png;A set of histograms of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
-Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable lead_time and the second with variable no_of_special_requests.
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition lead_time <= 151.5 and the second with the condition no_of_special_requests <= 2.5.
 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.
@@ -110,7 +110,7 @@ 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 booking_status.
 Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
-StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable basic_needs and the second with variable bullying.
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition basic_needs <= 3.5 and the second with the condition bullying <= 1.5.
 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.
@@ -123,7 +123,7 @@ 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 stress_level.
 StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 StressLevelDataset_histograms_numeric.png;A set of histograms of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
-WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable density and the second with variable chlorides.
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition density <= 1.0 and the second with the condition chlorides <= 0.08.
 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.
@@ -135,7 +135,7 @@ WineQT_boxplots.png;A set of boxplots of the variables ['fixed acidity', 'volati
 WineQT_class_histogram.png;A bar chart showing the distribution of the target variable quality.
 WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 WineQT_histograms_numeric.png;A set of histograms of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
-loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Loan_Amount_Term and the second with variable ApplicantIncome.
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Loan_Amount_Term <= 420.0 and the second with the condition ApplicantIncome <= 1519.0.
 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.
@@ -150,7 +150,7 @@ loan_data_mv.png;A bar chart showing the number of missing values per variable o
 loan_data_class_histogram.png;A bar chart showing the distribution of the target variable Loan_Status.
 loan_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 loan_data_histograms_numeric.png;A set of histograms of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
-Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Area and the second with variable AspectRation.
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Area <= 39172.5 and the second with the condition AspectRation <= 1.86.
 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.
@@ -162,7 +162,7 @@ Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables ['Area', 'Perim
 Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable Class.
 Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
-credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable existing_credits and the second with variable residence_since.
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition existing_credits <= 1.5 and the second with the condition residence_since <= 3.5.
 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.
@@ -176,7 +176,7 @@ 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 class.
 credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 credit_customers_histograms_numeric.png;A set of histograms of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
-weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rainfall and the second with variable Pressure3pm.
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Rainfall <= 0.1 and the second with the condition Pressure3pm <= 1009.65.
 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.
@@ -191,7 +191,7 @@ weatherAUS_mv.png;A bar chart showing the number of missing values per variable
 weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable RainTomorrow.
 weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 weatherAUS_histograms_numeric.png;A set of histograms of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
-car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable displacement and the second with variable height.
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition displacement <= 1196.5 and the second with the condition height <= 1519.0.
 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.
@@ -205,7 +205,7 @@ car_insurance_histograms_symbolic.png;A set of bar charts of the variables ['are
 car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable is_claim.
 car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 car_insurance_histograms_numeric.png;A set of histograms of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
-heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable slope and the second with variable restecg.
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition slope <= 1.5 and the second with the condition restecg <= 0.5.
 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.
@@ -219,7 +219,7 @@ heart_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'fbs'
 heart_class_histogram.png;A bar chart showing the distribution of the target variable target.
 heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 heart_histograms_numeric.png;A set of histograms of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
-Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable perimeter_mean and the second with variable texture_worst.
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition perimeter_mean <= 90.47 and the second with the condition texture_worst <= 27.89.
 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.
@@ -232,7 +232,7 @@ Breast_Cancer_boxplots.png;A set of boxplots of the variables ['texture_mean', '
 Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable diagnosis.
 Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Breast_Cancer_histograms_numeric.png;A set of histograms of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
-e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Prior_purchases and the second with variable Customer_care_calls.
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Prior_purchases <= 3.5 and the second with the condition Customer_care_calls <= 4.5.
 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.
@@ -246,7 +246,7 @@ e-commerce_histograms_symbolic.png;A set of bar charts of the variables ['Wareho
 e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable ReachedOnTime.
 e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 e-commerce_histograms_numeric.png;A set of histograms of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
-maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rotational speed [rpm] and the second with variable Torque [Nm].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Rotational speed [rpm] <= 1381.5 and the second with the condition Torque [Nm] <= 65.05.
 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.
@@ -260,7 +260,7 @@ maintenance_histograms_symbolic.png;A set of bar charts of the variables ['Type'
 maintenance_class_histogram.png;A bar chart showing the distribution of the target variable Machine_failure.
 maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 maintenance_histograms_numeric.png;A set of histograms of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
-Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable NumOfProducts.
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Age <= 42.5 and the second with the condition NumOfProducts <= 2.5.
 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.
@@ -274,7 +274,7 @@ Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables ['G
 Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable Exited.
 Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Churn_Modelling_histograms_numeric.png;A set of histograms of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
-vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable MAJORSKEWNESS and the second with variable CIRCULARITY.
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition MAJORSKEWNESS <= 74.5 and the second with the condition CIRCULARITY <= 49.5.
 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.
@@ -286,7 +286,7 @@ vehicle_boxplots.png;A set of boxplots of the variables ['COMPACTNESS', 'CIRCULA
 vehicle_class_histogram.png;A bar chart showing the distribution of the target variable target.
 vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 vehicle_histograms_numeric.png;A set of histograms of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
-adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable hours-per-week and the second with variable capital-loss.
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition hours-per-week <= 41.5 and the second with the condition capital-loss <= 1820.5.
 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.
@@ -300,7 +300,7 @@ adult_histograms_symbolic.png;A set of bar charts of the variables ['workclass',
 adult_class_histogram.png;A bar chart showing the distribution of the target variable income.
 adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 adult_histograms_numeric.png;A set of histograms of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
-Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable CARDIOVASCULAR and the second with variable ASHTMA.
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition CARDIOVASCULAR <= 50.0 and the second with the condition ASHTMA <= 1.5.
 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.
@@ -314,7 +314,7 @@ Covid_Data_histograms_symbolic.png;A set of bar charts of the variables ['USMER'
 Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable CLASSIFICATION.
 Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Covid_Data_histograms_numeric.png;A set of histograms of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
-sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable dec and the second with variable mjd.
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition dec <= 22.21 and the second with the condition mjd <= 55090.5.
 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.
@@ -326,7 +326,7 @@ sky_survey_boxplots.png;A set of boxplots of the variables ['ra', 'dec', 'run',
 sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable class.
 sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 sky_survey_histograms_numeric.png;A set of histograms of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
-Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Total phenols and the second with variable Proanthocyanins.
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Total phenols <= 2.36 and the second with the condition Proanthocyanins <= 1.58.
 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.
@@ -338,7 +338,7 @@ Wine_boxplots.png;A set of boxplots of the variables ['Alcohol', 'Malic acid', '
 Wine_class_histogram.png;A bar chart showing the distribution of the target variable Class.
 Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Wine_histograms_numeric.png;A set of histograms of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
-water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Hardness and the second with variable Chloramines.
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Hardness <= 278.29 and the second with the condition Chloramines <= 6.7.
 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.
@@ -352,7 +352,7 @@ water_potability_mv.png;A bar chart showing the number of missing values per var
 water_potability_class_histogram.png;A bar chart showing the distribution of the target variable Potability.
 water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 water_potability_histograms_numeric.png;A set of histograms of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
-abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Height and the second with variable Diameter.
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Height <= 0.13 and the second with the condition Diameter <= 0.45.
 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.
@@ -364,7 +364,7 @@ abalone_boxplots.png;A set of boxplots of the variables ['Length', 'Diameter', '
 abalone_class_histogram.png;A bar chart showing the distribution of the target variable Sex.
 abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 abalone_histograms_numeric.png;A set of histograms of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
-smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable SMK_stat_type_cd and the second with variable gamma_GTP.
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition SMK_stat_type_cd <= 1.5 and the second with the condition gamma_GTP <= 35.5.
 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.
@@ -378,7 +378,7 @@ 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 DRK_YN.
 smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 smoking_drinking_histograms_numeric.png;A set of histograms of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
-BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable skewness and the second with variable curtosis.
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition skewness <= 5.16 and the second with the condition curtosis <= 0.19.
 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.
@@ -391,7 +391,7 @@ BankNoteAuthentication_boxplots.png;A set of boxplots of the variables ['varianc
 BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable class.
 BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
-Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first and second decisions are made with variable PetalWidthCm.
+Iris_decision_tree.png;
 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.
@@ -403,7 +403,7 @@ Iris_boxplots.png;A set of boxplots of the variables ['SepalLengthCm', 'SepalWid
 Iris_class_histogram.png;A bar chart showing the distribution of the target variable Species.
 Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Iris_histograms_numeric.png;A set of histograms of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
-phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable int_memory and the second with variable mobile_wt.
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition int_memory <= 30.5 and the second with the condition mobile_wt <= 91.5.
 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.
@@ -416,7 +416,7 @@ phone_histograms_symbolic.png;A set of bar charts of the variables ['blue', 'dua
 phone_class_histogram.png;A bar chart showing the distribution of the target variable price_range.
 phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 phone_histograms_numeric.png;A set of histograms of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
-Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Pclass and the second with variable Parch.
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Pclass <= 2.5 and the second with the condition Parch <= 0.5.
 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.
@@ -431,7 +431,7 @@ Titanic_mv.png;A bar chart showing the number of missing values per variable of
 Titanic_class_histogram.png;A bar chart showing the distribution of the target variable Survived.
 Titanic_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Titanic_histograms_numeric.png;A set of histograms of the variables ['Pclass', 'Age', 'SibSp', 'Parch', 'Fare'].
-apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Juiciness and the second with variable Crunchiness.
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Juiciness <= -0.3 and the second with the condition Crunchiness <= 2.25.
 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.
@@ -444,7 +444,7 @@ apple_quality_boxplots.png;A set of boxplots of the variables ['Size', 'Weight',
 apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable Quality.
 apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 apple_quality_histograms_numeric.png;A set of histograms of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
-Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable JoiningYear and the second with variable ExperienceInCurrentDomain.
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition JoiningYear <= 2017.5 and the second with the condition ExperienceInCurrentDomain <= 3.5.
 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.

desc_dataset_test.csv

@@ -1,5 +1,5 @@
 Chart;description
-Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Pclass and the second with variable Parch.
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Pclass <= 2.5 and the second with the condition Parch <= 0.5.
 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.

desc_dataset_train.csv

@@ -1,5 +1,5 @@
 Chart;description
-ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable FAF and the second with variable Height.
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition FAF <= 2.0 and the second with the condition Height <= 1.72.
 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.
@@ -12,7 +12,7 @@ ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables ['CA
 ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable NObeyesdad.
 ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 ObesityDataSet_histograms_numeric.png;A set of histograms of the variables ['Age', 'Height', 'Weight', 'FCVC', 'NCP', 'CH2O', 'FAF', 'TUE'].
-customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Family_Size and the second with variable Work_Experience.
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Family_Size <= 2.5 and the second with the condition Work_Experience <= 9.5.
 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.
@@ -26,7 +26,7 @@ customer_segmentation_mv.png;A bar chart showing the number of missing values pe
 customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable Segmentation.
 customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 customer_segmentation_histograms_numeric.png;A set of histograms of the variables ['Age', 'Work_Experience', 'Family_Size'].
-urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable pH.
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Age <= 0.1 and the second with the condition pH <= 5.5.
 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.
@@ -41,7 +41,7 @@ urinalysis_tests_mv.png;A bar chart showing the number of missing values per var
 urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable Diagnosis.
 urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 urinalysis_tests_histograms_numeric.png;A set of histograms of the variables ['Age', 'pH', 'Specific Gravity'].
-detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Ic and the second with variable Vb.
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Ic <= 71.01 and the second with the condition Vb <= -0.37.
 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.
@@ -54,7 +54,7 @@ detect_dataset_boxplots.png;A set of boxplots of the variables ['Ia', 'Ib', 'Ic'
 detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable Output.
 detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 detect_dataset_histograms_numeric.png;A set of histograms of the variables ['Ia', 'Ib', 'Ic', 'Va', 'Vb', 'Vc'].
-diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable BMI and the second with variable Age.
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition BMI <= 29.85 and the second with the condition Age <= 27.5.
 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.
@@ -67,7 +67,7 @@ diabetes_boxplots.png;A set of boxplots of the variables ['Pregnancies', 'Glucos
 diabetes_class_histogram.png;A bar chart showing the distribution of the target variable Outcome.
 diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 diabetes_histograms_numeric.png;A set of histograms of the variables ['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin', 'BMI', 'DiabetesPedigreeFunction', 'Age'].
-Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable ssc_p and the second with variable hsc_p.
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition ssc_p <= 60.09 and the second with the condition hsc_p <= 70.24.
 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.
@@ -81,7 +81,7 @@ Placement_histograms_symbolic.png;A set of bar charts of the variables ['hsc_s',
 Placement_class_histogram.png;A bar chart showing the distribution of the target variable status.
 Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Placement_histograms_numeric.png;A set of histograms of the variables ['ssc_p', 'hsc_p', 'degree_p', 'etest_p', 'mba_p'].
-Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Alkphos and the second with variable Sgot.
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Alkphos <= 211.5 and the second with the condition Sgot <= 26.5.
 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.
@@ -96,7 +96,7 @@ Liver_Patient_mv.png;A bar chart showing the number of missing values per variab
 Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable Selector.
 Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Liver_Patient_histograms_numeric.png;A set of histograms of the variables ['Age', 'TB', 'DB', 'Alkphos', 'Sgpt', 'Sgot', 'TP', 'ALB', 'AG_Ratio'].
-Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable lead_time and the second with variable no_of_special_requests.
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition lead_time <= 151.5 and the second with the condition no_of_special_requests <= 2.5.
 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.
@@ -110,7 +110,7 @@ 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 booking_status.
 Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables ['no_of_adults', 'no_of_children', 'no_of_weekend_nights', 'no_of_week_nights', 'lead_time', 'arrival_month', 'arrival_date', 'avg_price_per_room', 'no_of_special_requests'].
-StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable basic_needs and the second with variable bullying.
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition basic_needs <= 3.5 and the second with the condition bullying <= 1.5.
 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.
@@ -123,7 +123,7 @@ 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 stress_level.
 StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 StressLevelDataset_histograms_numeric.png;A set of histograms of the variables ['anxiety_level', 'self_esteem', 'depression', 'headache', 'sleep_quality', 'breathing_problem', 'living_conditions', 'basic_needs', 'study_load', 'bullying'].
-WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable density and the second with variable chlorides.
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition density <= 1.0 and the second with the condition chlorides <= 0.08.
 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.
@@ -135,7 +135,7 @@ WineQT_boxplots.png;A set of boxplots of the variables ['fixed acidity', 'volati
 WineQT_class_histogram.png;A bar chart showing the distribution of the target variable quality.
 WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 WineQT_histograms_numeric.png;A set of histograms of the variables ['fixed acidity', 'volatile acidity', 'citric acid', 'residual sugar', 'chlorides', 'free sulfur dioxide', 'total sulfur dioxide', 'density', 'pH', 'sulphates', 'alcohol'].
-loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Loan_Amount_Term and the second with variable ApplicantIncome.
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Loan_Amount_Term <= 420.0 and the second with the condition ApplicantIncome <= 1519.0.
 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.
@@ -150,7 +150,7 @@ loan_data_mv.png;A bar chart showing the number of missing values per variable o
 loan_data_class_histogram.png;A bar chart showing the distribution of the target variable Loan_Status.
 loan_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 loan_data_histograms_numeric.png;A set of histograms of the variables ['ApplicantIncome', 'CoapplicantIncome', 'LoanAmount', 'Loan_Amount_Term'].
-Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Area and the second with variable AspectRation.
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Area <= 39172.5 and the second with the condition AspectRation <= 1.86.
 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.
@@ -162,7 +162,7 @@ Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables ['Area', 'Perim
 Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable Class.
 Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables ['Area', 'Perimeter', 'MinorAxisLength', 'AspectRation', 'Eccentricity', 'EquivDiameter', 'Extent', 'Solidity', 'roundness', 'ShapeFactor1'].
-credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable existing_credits and the second with variable residence_since.
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition existing_credits <= 1.5 and the second with the condition residence_since <= 3.5.
 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.
@@ -176,7 +176,7 @@ 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 class.
 credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 credit_customers_histograms_numeric.png;A set of histograms of the variables ['duration', 'credit_amount', 'installment_commitment', 'residence_since', 'age', 'existing_credits'].
-weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rainfall and the second with variable Pressure3pm.
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Rainfall <= 0.1 and the second with the condition Pressure3pm <= 1009.65.
 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.
@@ -191,7 +191,7 @@ weatherAUS_mv.png;A bar chart showing the number of missing values per variable
 weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable RainTomorrow.
 weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 weatherAUS_histograms_numeric.png;A set of histograms of the variables ['Rainfall', 'WindSpeed9am', 'Pressure9am', 'Pressure3pm', 'Cloud9am', 'Cloud3pm', 'Temp3pm'].
-car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable displacement and the second with variable height.
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition displacement <= 1196.5 and the second with the condition height <= 1519.0.
 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.
@@ -205,7 +205,7 @@ car_insurance_histograms_symbolic.png;A set of bar charts of the variables ['are
 car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable is_claim.
 car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 car_insurance_histograms_numeric.png;A set of histograms of the variables ['policy_tenure', 'age_of_car', 'age_of_policyholder', 'airbags', 'displacement', 'length', 'width', 'height', 'gross_weight'].
-heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable slope and the second with variable restecg.
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition slope <= 1.5 and the second with the condition restecg <= 0.5.
 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.
@@ -219,7 +219,7 @@ heart_histograms_symbolic.png;A set of bar charts of the variables ['sex', 'fbs'
 heart_class_histogram.png;A bar chart showing the distribution of the target variable target.
 heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 heart_histograms_numeric.png;A set of histograms of the variables ['age', 'cp', 'trestbps', 'chol', 'restecg', 'thalach', 'oldpeak', 'slope', 'ca', 'thal'].
-Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable perimeter_mean and the second with variable texture_worst.
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition perimeter_mean <= 90.47 and the second with the condition texture_worst <= 27.89.
 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.
@@ -232,7 +232,7 @@ Breast_Cancer_boxplots.png;A set of boxplots of the variables ['texture_mean', '
 Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable diagnosis.
 Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Breast_Cancer_histograms_numeric.png;A set of histograms of the variables ['texture_mean', 'perimeter_mean', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se', 'symmetry_se', 'radius_worst', 'texture_worst', 'perimeter_worst'].
-e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Prior_purchases and the second with variable Customer_care_calls.
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Prior_purchases <= 3.5 and the second with the condition Customer_care_calls <= 4.5.
 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.
@@ -246,7 +246,7 @@ e-commerce_histograms_symbolic.png;A set of bar charts of the variables ['Wareho
 e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable ReachedOnTime.
 e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 e-commerce_histograms_numeric.png;A set of histograms of the variables ['Customer_care_calls', 'Customer_rating', 'Cost_of_the_Product', 'Prior_purchases', 'Discount_offered', 'Weight_in_gms'].
-maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Rotational speed [rpm] and the second with variable Torque [Nm].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Rotational speed [rpm] <= 1381.5 and the second with the condition Torque [Nm] <= 65.05.
 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.
@@ -260,7 +260,7 @@ maintenance_histograms_symbolic.png;A set of bar charts of the variables ['Type'
 maintenance_class_histogram.png;A bar chart showing the distribution of the target variable Machine_failure.
 maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 maintenance_histograms_numeric.png;A set of histograms of the variables ['Air temperature [K]', 'Process temperature [K]', 'Rotational speed [rpm]', 'Torque [Nm]', 'Tool wear [min]'].
-Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Age and the second with variable NumOfProducts.
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Age <= 42.5 and the second with the condition NumOfProducts <= 2.5.
 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.
@@ -274,7 +274,7 @@ Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables ['G
 Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable Exited.
 Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Churn_Modelling_histograms_numeric.png;A set of histograms of the variables ['CreditScore', 'Age', 'Tenure', 'Balance', 'NumOfProducts', 'EstimatedSalary'].
-vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable MAJORSKEWNESS and the second with variable CIRCULARITY.
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition MAJORSKEWNESS <= 74.5 and the second with the condition CIRCULARITY <= 49.5.
 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.
@@ -286,7 +286,7 @@ vehicle_boxplots.png;A set of boxplots of the variables ['COMPACTNESS', 'CIRCULA
 vehicle_class_histogram.png;A bar chart showing the distribution of the target variable target.
 vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 vehicle_histograms_numeric.png;A set of histograms of the variables ['COMPACTNESS', 'CIRCULARITY', 'DISTANCE CIRCULARITY', 'RADIUS RATIO', 'MAJORVARIANCE', 'MINORVARIANCE', 'GYRATIONRADIUS', 'MAJORSKEWNESS', 'MINORSKEWNESS', 'MINORKURTOSIS', 'MAJORKURTOSIS'].
-adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable hours-per-week and the second with variable capital-loss.
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition hours-per-week <= 41.5 and the second with the condition capital-loss <= 1820.5.
 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.
@@ -300,7 +300,7 @@ adult_histograms_symbolic.png;A set of bar charts of the variables ['workclass',
 adult_class_histogram.png;A bar chart showing the distribution of the target variable income.
 adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 adult_histograms_numeric.png;A set of histograms of the variables ['age', 'fnlwgt', 'educational-num', 'capital-gain', 'capital-loss', 'hours-per-week'].
-Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable CARDIOVASCULAR and the second with variable ASHTMA.
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition CARDIOVASCULAR <= 50.0 and the second with the condition ASHTMA <= 1.5.
 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.
@@ -314,7 +314,7 @@ Covid_Data_histograms_symbolic.png;A set of bar charts of the variables ['USMER'
 Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable CLASSIFICATION.
 Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Covid_Data_histograms_numeric.png;A set of histograms of the variables ['MEDICAL_UNIT', 'PNEUMONIA', 'AGE', 'PREGNANT', 'COPD', 'ASTHMA', 'HIPERTENSION', 'OTHER_DISEASE', 'CARDIOVASCULAR', 'RENAL_CHRONIC', 'TOBACCO', 'ICU'].
-sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable dec and the second with variable mjd.
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition dec <= 22.21 and the second with the condition mjd <= 55090.5.
 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.
@@ -326,7 +326,7 @@ sky_survey_boxplots.png;A set of boxplots of the variables ['ra', 'dec', 'run',
 sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable class.
 sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 sky_survey_histograms_numeric.png;A set of histograms of the variables ['ra', 'dec', 'run', 'camcol', 'field', 'redshift', 'plate', 'mjd'].
-Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Total phenols and the second with variable Proanthocyanins.
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Total phenols <= 2.36 and the second with the condition Proanthocyanins <= 1.58.
 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.
@@ -338,7 +338,7 @@ Wine_boxplots.png;A set of boxplots of the variables ['Alcohol', 'Malic acid', '
 Wine_class_histogram.png;A bar chart showing the distribution of the target variable Class.
 Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Wine_histograms_numeric.png;A set of histograms of the variables ['Alcohol', 'Malic acid', 'Ash', 'Alcalinity of ash', 'Total phenols', 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', 'Color intensity', 'Hue', 'OD280-OD315 of diluted wines'].
-water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Hardness and the second with variable Chloramines.
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Hardness <= 278.29 and the second with the condition Chloramines <= 6.7.
 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.
@@ -352,7 +352,7 @@ water_potability_mv.png;A bar chart showing the number of missing values per var
 water_potability_class_histogram.png;A bar chart showing the distribution of the target variable Potability.
 water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 water_potability_histograms_numeric.png;A set of histograms of the variables ['ph', 'Hardness', 'Chloramines', 'Sulfate', 'Conductivity', 'Trihalomethanes', 'Turbidity'].
-abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Height and the second with variable Diameter.
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Height <= 0.13 and the second with the condition Diameter <= 0.45.
 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.
@@ -364,7 +364,7 @@ abalone_boxplots.png;A set of boxplots of the variables ['Length', 'Diameter', '
 abalone_class_histogram.png;A bar chart showing the distribution of the target variable Sex.
 abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 abalone_histograms_numeric.png;A set of histograms of the variables ['Length', 'Diameter', 'Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'].
-smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable SMK_stat_type_cd and the second with variable gamma_GTP.
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition SMK_stat_type_cd <= 1.5 and the second with the condition gamma_GTP <= 35.5.
 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.
@@ -378,7 +378,7 @@ 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 DRK_YN.
 smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 smoking_drinking_histograms_numeric.png;A set of histograms of the variables ['age', 'height', 'weight', 'waistline', 'SBP', 'BLDS', 'tot_chole', 'LDL_chole', 'triglyceride', 'hemoglobin', 'gamma_GTP', 'SMK_stat_type_cd'].
-BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable skewness and the second with variable curtosis.
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition skewness <= 5.16 and the second with the condition curtosis <= 0.19.
 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.
@@ -391,7 +391,7 @@ BankNoteAuthentication_boxplots.png;A set of boxplots of the variables ['varianc
 BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable class.
 BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables ['variance', 'skewness', 'curtosis', 'entropy'].
-Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first and second decisions are made with variable PetalWidthCm.
+Iris_decision_tree.png;
 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.
@@ -403,7 +403,7 @@ Iris_boxplots.png;A set of boxplots of the variables ['SepalLengthCm', 'SepalWid
 Iris_class_histogram.png;A bar chart showing the distribution of the target variable Species.
 Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Iris_histograms_numeric.png;A set of histograms of the variables ['SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm'].
-phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable int_memory and the second with variable mobile_wt.
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition int_memory <= 30.5 and the second with the condition mobile_wt <= 91.5.
 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.
@@ -416,7 +416,7 @@ phone_histograms_symbolic.png;A set of bar charts of the variables ['blue', 'dua
 phone_class_histogram.png;A bar chart showing the distribution of the target variable price_range.
 phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 phone_histograms_numeric.png;A set of histograms of the variables ['battery_power', 'fc', 'int_memory', 'mobile_wt', 'n_cores', 'pc', 'px_height', 'px_width', 'ram', 'sc_h', 'sc_w', 'talk_time'].
-apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable Juiciness and the second with variable Crunchiness.
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition Juiciness <= -0.3 and the second with the condition Crunchiness <= 2.25.
 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.
@@ -429,7 +429,7 @@ apple_quality_boxplots.png;A set of boxplots of the variables ['Size', 'Weight',
 apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable Quality.
 apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 apple_quality_histograms_numeric.png;A set of histograms of the variables ['Size', 'Weight', 'Sweetness', 'Crunchiness', 'Juiciness', 'Ripeness', 'Acidity'].
-Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable JoiningYear and the second with variable ExperienceInCurrentDomain.
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition JoiningYear <= 2017.5 and the second with the condition ExperienceInCurrentDomain <= 3.5.
 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.