Chart;description
ObesityDataSet_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
ObesityDataSet_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
ObesityDataSet_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
ObesityDataSet_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
ObesityDataSet_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
ObesityDataSet_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
ObesityDataSet_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
ObesityDataSet_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
ObesityDataSet_boxplots.png;A set of boxplots of the variables [].
ObesityDataSet_histograms_symbolic.png;A set of bar charts of the variables [].
ObesityDataSet_class_histogram.png;A bar chart showing the distribution of the target variable [].
ObesityDataSet_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
ObesityDataSet_histograms_numeric.png;A set of histograms of the variables [].
customer_segmentation_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
customer_segmentation_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
customer_segmentation_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
customer_segmentation_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
customer_segmentation_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
customer_segmentation_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
customer_segmentation_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
customer_segmentation_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
customer_segmentation_boxplots.png;A set of boxplots of the variables [].
customer_segmentation_histograms_symbolic.png;A set of bar charts of the variables [].
customer_segmentation_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
customer_segmentation_class_histogram.png;A bar chart showing the distribution of the target variable [].
customer_segmentation_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
customer_segmentation_histograms_numeric.png;A set of histograms of the variables [].
urinalysis_tests_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
urinalysis_tests_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
urinalysis_tests_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
urinalysis_tests_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
urinalysis_tests_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
urinalysis_tests_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
urinalysis_tests_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
urinalysis_tests_pca.png;A bar chart showing the explained variance ratio of 3 principal components.
urinalysis_tests_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
urinalysis_tests_boxplots.png;A set of boxplots of the variables [].
urinalysis_tests_histograms_symbolic.png;A set of bar charts of the variables [].
urinalysis_tests_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
urinalysis_tests_class_histogram.png;A bar chart showing the distribution of the target variable [].
urinalysis_tests_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
urinalysis_tests_histograms_numeric.png;A set of histograms of the variables [].
detect_dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
detect_dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
detect_dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
detect_dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
detect_dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
detect_dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
detect_dataset_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
detect_dataset_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
detect_dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
detect_dataset_boxplots.png;A set of boxplots of the variables [].
detect_dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
detect_dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
detect_dataset_histograms_numeric.png;A set of histograms of the variables [].
diabetes_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
diabetes_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
diabetes_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
diabetes_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
diabetes_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
diabetes_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
diabetes_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
diabetes_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
diabetes_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
diabetes_boxplots.png;A set of boxplots of the variables [].
diabetes_class_histogram.png;A bar chart showing the distribution of the target variable [].
diabetes_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
diabetes_histograms_numeric.png;A set of histograms of the variables [].
Placement_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Placement_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Placement_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Placement_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Placement_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Placement_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Placement_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Placement_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
Placement_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Placement_boxplots.png;A set of boxplots of the variables [].
Placement_histograms_symbolic.png;A set of bar charts of the variables [].
Placement_class_histogram.png;A bar chart showing the distribution of the target variable [].
Placement_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Placement_histograms_numeric.png;A set of histograms of the variables [].
Liver_Patient_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Liver_Patient_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Liver_Patient_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Liver_Patient_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Liver_Patient_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Liver_Patient_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Liver_Patient_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Liver_Patient_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
Liver_Patient_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Liver_Patient_boxplots.png;A set of boxplots of the variables [].
Liver_Patient_histograms_symbolic.png;A set of bar charts of the variables [].
Liver_Patient_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
Liver_Patient_class_histogram.png;A bar chart showing the distribution of the target variable [].
Liver_Patient_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Liver_Patient_histograms_numeric.png;A set of histograms of the variables [].
Hotel_Reservations_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Hotel_Reservations_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Hotel_Reservations_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Hotel_Reservations_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Hotel_Reservations_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Hotel_Reservations_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Hotel_Reservations_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Hotel_Reservations_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
Hotel_Reservations_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Hotel_Reservations_boxplots.png;A set of boxplots of the variables [].
Hotel_Reservations_histograms_symbolic.png;A set of bar charts of the variables [].
Hotel_Reservations_class_histogram.png;A bar chart showing the distribution of the target variable [].
Hotel_Reservations_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Hotel_Reservations_histograms_numeric.png;A set of histograms of the variables [].
StressLevelDataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
StressLevelDataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
StressLevelDataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
StressLevelDataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
StressLevelDataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
StressLevelDataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
StressLevelDataset_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
StressLevelDataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
StressLevelDataset_boxplots.png;A set of boxplots of the variables [].
StressLevelDataset_histograms_symbolic.png;A set of bar charts of the variables [].
StressLevelDataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
StressLevelDataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
StressLevelDataset_histograms_numeric.png;A set of histograms of the variables [].
WineQT_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
WineQT_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
WineQT_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
WineQT_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
WineQT_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
WineQT_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
WineQT_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
WineQT_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
WineQT_boxplots.png;A set of boxplots of the variables [].
WineQT_class_histogram.png;A bar chart showing the distribution of the target variable [].
WineQT_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
WineQT_histograms_numeric.png;A set of histograms of the variables [].
loan_data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
loan_data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
loan_data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
loan_data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
loan_data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
loan_data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
loan_data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
loan_data_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
loan_data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
loan_data_boxplots.png;A set of boxplots of the variables [].
loan_data_histograms_symbolic.png;A set of bar charts of the variables [].
loan_data_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
loan_data_class_histogram.png;A bar chart showing the distribution of the target variable [].
loan_data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
loan_data_histograms_numeric.png;A set of histograms of the variables [].
Dry_Bean_Dataset_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Dry_Bean_Dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Dry_Bean_Dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Dry_Bean_Dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Dry_Bean_Dataset_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Dry_Bean_Dataset_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Dry_Bean_Dataset_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
Dry_Bean_Dataset_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Dry_Bean_Dataset_boxplots.png;A set of boxplots of the variables [].
Dry_Bean_Dataset_class_histogram.png;A bar chart showing the distribution of the target variable [].
Dry_Bean_Dataset_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Dry_Bean_Dataset_histograms_numeric.png;A set of histograms of the variables [].
credit_customers_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
credit_customers_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
credit_customers_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
credit_customers_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
credit_customers_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
credit_customers_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
credit_customers_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
credit_customers_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
credit_customers_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
credit_customers_boxplots.png;A set of boxplots of the variables [].
credit_customers_histograms_symbolic.png;A set of bar charts of the variables [].
credit_customers_class_histogram.png;A bar chart showing the distribution of the target variable [].
credit_customers_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
credit_customers_histograms_numeric.png;A set of histograms of the variables [].
weatherAUS_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
weatherAUS_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
weatherAUS_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
weatherAUS_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
weatherAUS_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
weatherAUS_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
weatherAUS_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
weatherAUS_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
weatherAUS_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
weatherAUS_boxplots.png;A set of boxplots of the variables [].
weatherAUS_histograms_symbolic.png;A set of bar charts of the variables [].
weatherAUS_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
weatherAUS_class_histogram.png;A bar chart showing the distribution of the target variable [].
weatherAUS_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
weatherAUS_histograms_numeric.png;A set of histograms of the variables [].
car_insurance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
car_insurance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
car_insurance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
car_insurance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
car_insurance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
car_insurance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
car_insurance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
car_insurance_pca.png;A bar chart showing the explained variance ratio of 9 principal components.
car_insurance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
car_insurance_boxplots.png;A set of boxplots of the variables [].
car_insurance_histograms_symbolic.png;A set of bar charts of the variables [].
car_insurance_class_histogram.png;A bar chart showing the distribution of the target variable [].
car_insurance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
car_insurance_histograms_numeric.png;A set of histograms of the variables [].
heart_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
heart_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
heart_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
heart_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
heart_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
heart_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
heart_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
heart_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
heart_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
heart_boxplots.png;A set of boxplots of the variables [].
heart_histograms_symbolic.png;A set of bar charts of the variables [].
heart_class_histogram.png;A bar chart showing the distribution of the target variable [].
heart_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
heart_histograms_numeric.png;A set of histograms of the variables [].
Breast_Cancer_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Breast_Cancer_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Breast_Cancer_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Breast_Cancer_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Breast_Cancer_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Breast_Cancer_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Breast_Cancer_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Breast_Cancer_pca.png;A bar chart showing the explained variance ratio of 10 principal components.
Breast_Cancer_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Breast_Cancer_boxplots.png;A set of boxplots of the variables [].
Breast_Cancer_class_histogram.png;A bar chart showing the distribution of the target variable [].
Breast_Cancer_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Breast_Cancer_histograms_numeric.png;A set of histograms of the variables [].
e-commerce_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
e-commerce_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
e-commerce_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
e-commerce_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
e-commerce_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
e-commerce_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
e-commerce_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
e-commerce_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
e-commerce_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
e-commerce_boxplots.png;A set of boxplots of the variables [].
e-commerce_histograms_symbolic.png;A set of bar charts of the variables [].
e-commerce_class_histogram.png;A bar chart showing the distribution of the target variable [].
e-commerce_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
e-commerce_histograms_numeric.png;A set of histograms of the variables [].
maintenance_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
maintenance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
maintenance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
maintenance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
maintenance_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
maintenance_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
maintenance_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
maintenance_pca.png;A bar chart showing the explained variance ratio of 5 principal components.
maintenance_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
maintenance_boxplots.png;A set of boxplots of the variables [].
maintenance_histograms_symbolic.png;A set of bar charts of the variables [].
maintenance_class_histogram.png;A bar chart showing the distribution of the target variable [].
maintenance_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
maintenance_histograms_numeric.png;A set of histograms of the variables [].
Churn_Modelling_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Churn_Modelling_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Churn_Modelling_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Churn_Modelling_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Churn_Modelling_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Churn_Modelling_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Churn_Modelling_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Churn_Modelling_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
Churn_Modelling_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Churn_Modelling_boxplots.png;A set of boxplots of the variables [].
Churn_Modelling_histograms_symbolic.png;A set of bar charts of the variables [].
Churn_Modelling_class_histogram.png;A bar chart showing the distribution of the target variable [].
Churn_Modelling_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Churn_Modelling_histograms_numeric.png;A set of histograms of the variables [].
vehicle_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
vehicle_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
vehicle_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
vehicle_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
vehicle_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
vehicle_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
vehicle_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
vehicle_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
vehicle_boxplots.png;A set of boxplots of the variables [].
vehicle_class_histogram.png;A bar chart showing the distribution of the target variable [].
vehicle_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
vehicle_histograms_numeric.png;A set of histograms of the variables [].
adult_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
adult_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
adult_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
adult_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
adult_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
adult_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
adult_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
adult_pca.png;A bar chart showing the explained variance ratio of 6 principal components.
adult_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
adult_boxplots.png;A set of boxplots of the variables [].
adult_histograms_symbolic.png;A set of bar charts of the variables [].
adult_class_histogram.png;A bar chart showing the distribution of the target variable [].
adult_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
adult_histograms_numeric.png;A set of histograms of the variables [].
Covid_Data_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Covid_Data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Covid_Data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Covid_Data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Covid_Data_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Covid_Data_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Covid_Data_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Covid_Data_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
Covid_Data_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Covid_Data_boxplots.png;A set of boxplots of the variables [].
Covid_Data_histograms_symbolic.png;A set of bar charts of the variables [].
Covid_Data_class_histogram.png;A bar chart showing the distribution of the target variable [].
Covid_Data_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Covid_Data_histograms_numeric.png;A set of histograms of the variables [].
sky_survey_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
sky_survey_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
sky_survey_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
sky_survey_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
sky_survey_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
sky_survey_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
sky_survey_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
sky_survey_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
sky_survey_boxplots.png;A set of boxplots of the variables [].
sky_survey_class_histogram.png;A bar chart showing the distribution of the target variable [].
sky_survey_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
sky_survey_histograms_numeric.png;A set of histograms of the variables [].
Wine_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Wine_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Wine_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Wine_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Wine_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Wine_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Wine_pca.png;A bar chart showing the explained variance ratio of 11 principal components.
Wine_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Wine_boxplots.png;A set of boxplots of the variables [].
Wine_class_histogram.png;A bar chart showing the distribution of the target variable [].
Wine_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Wine_histograms_numeric.png;A set of histograms of the variables [].
water_potability_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
water_potability_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
water_potability_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
water_potability_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
water_potability_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
water_potability_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
water_potability_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
water_potability_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
water_potability_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
water_potability_boxplots.png;A set of boxplots of the variables [].
water_potability_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: [].
water_potability_class_histogram.png;A bar chart showing the distribution of the target variable [].
water_potability_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
water_potability_histograms_numeric.png;A set of histograms of the variables [].
abalone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
abalone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
abalone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
abalone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
abalone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
abalone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
abalone_pca.png;A bar chart showing the explained variance ratio of 8 principal components.
abalone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
abalone_boxplots.png;A set of boxplots of the variables [].
abalone_class_histogram.png;A bar chart showing the distribution of the target variable [].
abalone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
abalone_histograms_numeric.png;A set of histograms of the variables [].
smoking_drinking_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
smoking_drinking_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
smoking_drinking_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
smoking_drinking_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
smoking_drinking_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
smoking_drinking_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
smoking_drinking_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
smoking_drinking_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
smoking_drinking_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
smoking_drinking_boxplots.png;A set of boxplots of the variables [].
smoking_drinking_histograms_symbolic.png;A set of bar charts of the variables [].
smoking_drinking_class_histogram.png;A bar chart showing the distribution of the target variable [].
smoking_drinking_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
smoking_drinking_histograms_numeric.png;A set of histograms of the variables [].
BankNoteAuthentication_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
BankNoteAuthentication_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
BankNoteAuthentication_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
BankNoteAuthentication_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
BankNoteAuthentication_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
BankNoteAuthentication_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
BankNoteAuthentication_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
BankNoteAuthentication_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
BankNoteAuthentication_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
BankNoteAuthentication_boxplots.png;A set of boxplots of the variables [].
BankNoteAuthentication_class_histogram.png;A bar chart showing the distribution of the target variable [].
BankNoteAuthentication_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
BankNoteAuthentication_histograms_numeric.png;A set of histograms of the variables [].
Iris_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Iris_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Iris_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Iris_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Iris_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Iris_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Iris_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
Iris_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Iris_boxplots.png;A set of boxplots of the variables [].
Iris_class_histogram.png;A bar chart showing the distribution of the target variable [].
Iris_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Iris_histograms_numeric.png;A set of histograms of the variables [].
phone_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
phone_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
phone_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
phone_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
phone_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
phone_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
phone_pca.png;A bar chart showing the explained variance ratio of 12 principal components.
phone_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
phone_boxplots.png;A set of boxplots of the variables [].
phone_histograms_symbolic.png;A set of bar charts of the variables [].
phone_class_histogram.png;A bar chart showing the distribution of the target variable [].
phone_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
phone_histograms_numeric.png;A set of histograms of the variables [].
apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
apple_quality_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
apple_quality_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
apple_quality_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
apple_quality_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
apple_quality_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
apple_quality_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
apple_quality_pca.png;A bar chart showing the explained variance ratio of 7 principal components.
apple_quality_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
apple_quality_boxplots.png;A set of boxplots of the variables [].
apple_quality_class_histogram.png;A bar chart showing the distribution of the target variable [].
apple_quality_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
apple_quality_histograms_numeric.png;A set of histograms of the variables [].
Employee_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable [].
Employee_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Employee_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Employee_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Employee_overfitting_knn.png;A multi-line chart showing the overfitting of k-nearest neighbors where the y-axis represents the accuracy and the x-axis represents the number of neighbors ranging from 1 to 23.
Employee_overfitting_decision_tree.png;A multi-line chart showing the overfitting of a decision tree where the y-axis represents the accuracy and the x-axis represents the max depth ranging from 2 to 25.
Employee_overfitting_dt_acc_rec.png;A multi-line chart showing the overfitting of decision tree where the y-axis represents the performance of both accuracy and recall and the x-axis represents the max depth ranging from 2 to 25.
Employee_pca.png;A bar chart showing the explained variance ratio of 4 principal components.
Employee_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].
Employee_boxplots.png;A set of boxplots of the variables [].
Employee_histograms_symbolic.png;A set of bar charts of the variables [].
Employee_class_histogram.png;A bar chart showing the distribution of the target variable [].
Employee_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset.
Employee_histograms_numeric.png;A set of histograms of the variables [].