Conditions

Img_Desc_Templates.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 [] and the second with variable [].
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable [].
 ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 ObesityDataSet_histograms_numeric.png;A set of histograms of the variables [].
-customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 customer_segmentation_histograms_numeric.png;A set of histograms of the variables [].
-urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 urinalysis_tests_histograms_numeric.png;A set of histograms of the variables [].
-detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
 detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 detect_dataset_histograms_numeric.png;A set of histograms of the variables [].
-diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 diabetes_class_histogram.png;A bar chart showing the distribution of the target variable [].
 diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 diabetes_histograms_numeric.png;A set of histograms of the variables [].
-Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Placement_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Placement_histograms_numeric.png;A set of histograms of the variables [].
-Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Liver_Patient_histograms_numeric.png;A set of histograms of the variables [].
-Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables [].
-StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 StressLevelDataset_histograms_numeric.png;A set of histograms of the variables [].
-WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 WineQT_class_histogram.png;A bar chart showing the distribution of the target variable [].
 WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 WineQT_histograms_numeric.png;A set of histograms of the variables [].
-loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 loan_data_histograms_numeric.png;A set of histograms of the variables [].
-Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables [].
-credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 credit_customers_histograms_numeric.png;A set of histograms of the variables [].
-weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 weatherAUS_histograms_numeric.png;A set of histograms of the variables [].
-car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable [].
 car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 car_insurance_histograms_numeric.png;A set of histograms of the variables [].
-heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 heart_class_histogram.png;A bar chart showing the distribution of the target variable [].
 heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 heart_histograms_numeric.png;A set of histograms of the variables [].
-Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Breast_Cancer_histograms_numeric.png;A set of histograms of the variables [].
-e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable [].
 e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 e-commerce_histograms_numeric.png;A set of histograms of the variables [].
-maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 maintenance_class_histogram.png;A bar chart showing the distribution of the target variable [].
 maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 maintenance_histograms_numeric.png;A set of histograms of the variables [].
-Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Churn_Modelling_histograms_numeric.png;A set of histograms of the variables [].
-vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 vehicle_class_histogram.png;A bar chart showing the distribution of the target variable [].
 vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 vehicle_histograms_numeric.png;A set of histograms of the variables [].
-adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 adult_class_histogram.png;A bar chart showing the distribution of the target variable [].
 adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 adult_histograms_numeric.png;A set of histograms of the variables [].
-Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Covid_Data_histograms_numeric.png;A set of histograms of the variables [].
-sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable [].
 sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 sky_survey_histograms_numeric.png;A set of histograms of the variables [].
-Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Wine_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Wine_histograms_numeric.png;A set of histograms of the variables [].
-water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 water_potability_histograms_numeric.png;A set of histograms of the variables [].
-abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 abalone_class_histogram.png;A bar chart showing the distribution of the target variable [].
 abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 abalone_histograms_numeric.png;A set of histograms of the variables [].
-smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 smoking_drinking_histograms_numeric.png;A set of histograms of the variables [].
-BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable [].
 BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables [].
-Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Iris_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Iris_histograms_numeric.png;A set of histograms of the variables [].
-phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 phone_class_histogram.png;A bar chart showing the distribution of the target variable [].
 phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 phone_histograms_numeric.png;A set of histograms of the variables [].
-Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Titanic_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Titanic_histograms_numeric.png;A set of histograms of the variables [].
-apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable [].
 apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 apple_quality_histograms_numeric.png;A set of histograms of the variables [].
-Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [] and the second with variable [].
+Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [] and the second with variable [].
+ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable [].
 ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 ObesityDataSet_histograms_numeric.png;A set of histograms of the variables [].
-customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 customer_segmentation_histograms_numeric.png;A set of histograms of the variables [].
-urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 urinalysis_tests_histograms_numeric.png;A set of histograms of the variables [].
-detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
 detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 detect_dataset_histograms_numeric.png;A set of histograms of the variables [].
-diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 diabetes_class_histogram.png;A bar chart showing the distribution of the target variable [].
 diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 diabetes_histograms_numeric.png;A set of histograms of the variables [].
-Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Placement_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Placement_histograms_numeric.png;A set of histograms of the variables [].
-Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Liver_Patient_histograms_numeric.png;A set of histograms of the variables [].
-Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables [].
-StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 StressLevelDataset_histograms_numeric.png;A set of histograms of the variables [].
-WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 WineQT_class_histogram.png;A bar chart showing the distribution of the target variable [].
 WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 WineQT_histograms_numeric.png;A set of histograms of the variables [].
-loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 loan_data_histograms_numeric.png;A set of histograms of the variables [].
-Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables [].
-credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 credit_customers_histograms_numeric.png;A set of histograms of the variables [].
-weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 weatherAUS_histograms_numeric.png;A set of histograms of the variables [].
-car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable [].
 car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 car_insurance_histograms_numeric.png;A set of histograms of the variables [].
-heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 heart_class_histogram.png;A bar chart showing the distribution of the target variable [].
 heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 heart_histograms_numeric.png;A set of histograms of the variables [].
-Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Breast_Cancer_histograms_numeric.png;A set of histograms of the variables [].
-e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable [].
 e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 e-commerce_histograms_numeric.png;A set of histograms of the variables [].
-maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 maintenance_class_histogram.png;A bar chart showing the distribution of the target variable [].
 maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 maintenance_histograms_numeric.png;A set of histograms of the variables [].
-Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Churn_Modelling_histograms_numeric.png;A set of histograms of the variables [].
-vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 vehicle_class_histogram.png;A bar chart showing the distribution of the target variable [].
 vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 vehicle_histograms_numeric.png;A set of histograms of the variables [].
-adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 adult_class_histogram.png;A bar chart showing the distribution of the target variable [].
 adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 adult_histograms_numeric.png;A set of histograms of the variables [].
-Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Covid_Data_histograms_numeric.png;A set of histograms of the variables [].
-sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable [].
 sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 sky_survey_histograms_numeric.png;A set of histograms of the variables [].
-Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Wine_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Wine_histograms_numeric.png;A set of histograms of the variables [].
-water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 water_potability_histograms_numeric.png;A set of histograms of the variables [].
-abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 abalone_class_histogram.png;A bar chart showing the distribution of the target variable [].
 abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 abalone_histograms_numeric.png;A set of histograms of the variables [].
-smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 smoking_drinking_histograms_numeric.png;A set of histograms of the variables [].
-BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable [].
 BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables [].
-Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 Iris_class_histogram.png;A bar chart showing the distribution of the target variable [].
 Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 Iris_histograms_numeric.png;A set of histograms of the variables [].
-phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 phone_class_histogram.png;A bar chart showing the distribution of the target variable [].
 phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 phone_histograms_numeric.png;A set of histograms of the variables [].
-apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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 [].
 apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable [].
 apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
 apple_quality_histograms_numeric.png;A set of histograms of the variables [].
-Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
+Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with the condition [] and the second with the condition [].
 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.