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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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

customer_segmentation_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].

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

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

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

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

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

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

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

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

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

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

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

urinalysis_tests_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Liver_Patient_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].

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

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

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

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