| 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 []. | |
| Titanic_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable []. | |
| Titanic_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000. | |
| Titanic_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002. | |
| Titanic_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002. | |
| Titanic_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. | |
| Titanic_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. | |
| Titanic_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. | |
| Titanic_pca.png;A bar chart showing the explained variance ratio of 5 principal components. | |
| Titanic_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are []. | |
| Titanic_boxplots.png;A set of boxplots of the variables []. | |
| Titanic_histograms_symbolic.png;A set of bar charts of the variables []. | |
| Titanic_mv.png;A bar chart showing the number of missing values per variable of the dataset. The variables that have missing values are: []. | |
| Titanic_class_histogram.png;A bar chart showing the distribution of the target variable []. | |
| Titanic_nr_records_nr_variables.png;A bar chart showing the number of records and variables of the dataset. | |
| Titanic_histograms_numeric.png;A set of histograms of the variables []. | |
| apple_quality_decision_tree.png;An image showing a decision tree with depth = 2 where the first decision is made with variable [] and the second with variable []. | |
| apple_quality_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 []. | |