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car_insurance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

car_insurance_boxplots.png;A set of boxplots of the variables [].

car_insurance_histograms_symbolic.png;A set of bar charts of the variables [].

car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable [].

car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

car_insurance_histograms_numeric.png;A set of histograms of the variables [].

heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

heart_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

heart_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

heart_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.

heart_pca.png;A bar chart showing the explained variance ratio of [] principal components.

heart_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

heart_boxplots.png;A set of boxplots of the variables [].

heart_histograms_symbolic.png;A set of bar charts of the variables [].

heart_class_histogram.png;A bar chart showing the distribution of the target variable [].

heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

heart_histograms_numeric.png;A set of histograms of the variables [].

Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

Breast_Cancer_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

Breast_Cancer_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

Breast_Cancer_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.

Breast_Cancer_pca.png;A bar chart showing the explained variance ratio of [] principal components.

Breast_Cancer_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

Breast_Cancer_boxplots.png;A set of boxplots of the variables [].

Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable [].

Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

Breast_Cancer_histograms_numeric.png;A set of histograms of the variables [].

e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

e-commerce_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

e-commerce_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

e-commerce_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.

e-commerce_pca.png;A bar chart showing the explained variance ratio of [] principal components.

e-commerce_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

e-commerce_boxplots.png;A set of boxplots of the variables [].

e-commerce_histograms_symbolic.png;A set of bar charts of the variables [].

e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable [].

e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

e-commerce_histograms_numeric.png;A set of histograms of the variables [].

maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

maintenance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

maintenance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

maintenance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.

maintenance_pca.png;A bar chart showing the explained variance ratio of [] principal components.

maintenance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

maintenance_boxplots.png;A set of boxplots of the variables [].

maintenance_histograms_symbolic.png;A set of bar charts of the variables [].

maintenance_class_histogram.png;A bar chart showing the distribution of the target variable [].

maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

maintenance_histograms_numeric.png;A set of histograms of the variables [].

Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

Churn_Modelling_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

Churn_Modelling_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

Churn_Modelling_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.

Churn_Modelling_pca.png;A bar chart showing the explained variance ratio of [] principal components.

Churn_Modelling_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

Churn_Modelling_boxplots.png;A set of boxplots of the variables [].

Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables [].

Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable [].

Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

Churn_Modelling_histograms_numeric.png;A set of histograms of the variables [].

vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

vehicle_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

vehicle_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

vehicle_pca.png;A bar chart showing the explained variance ratio of [] principal components.

vehicle_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

vehicle_boxplots.png;A set of boxplots of the variables [].

vehicle_class_histogram.png;A bar chart showing the distribution of the target variable [].

vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.

vehicle_histograms_numeric.png;A set of histograms of the variables [].

adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with 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.

adult_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.

adult_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.

adult_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.

adult_pca.png;A bar chart showing the explained variance ratio of [] principal components.

adult_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

adult_boxplots.png;A set of boxplots of the variables [].

adult_histograms_symbolic.png;A set of bar charts of the variables [].

adult_class_histogram.png;A bar chart showing the distribution of the target variable [].

adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.