eduvedras/CaptionTemplatesDataset · Datasets at Hugging Face

Chart;description stringlengths 56 243
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 [] 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 the condition [] and the second with the condition [].
StressLevelDataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
StressLevelDataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
StressLevelDataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] 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 the condition [] and the second with the condition [].
WineQT_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
WineQT_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
WineQT_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] 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 the condition [] and the second with the condition [].
loan_data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
loan_data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
loan_data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] 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 the condition [] and the second with the condition [].
Dry_Bean_Dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
Dry_Bean_Dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
Dry_Bean_Dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] 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 the condition [] and the second with the condition [].
credit_customers_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
credit_customers_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
credit_customers_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] 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 the condition [] and the second with the condition [].
weatherAUS_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
weatherAUS_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
weatherAUS_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] 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 the condition [] and the second with the condition [].
car_insurance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.
car_insurance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
car_insurance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.
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 [] principal components.

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 [] 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 the condition [] and the second with the condition [].

StressLevelDataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

StressLevelDataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

StressLevelDataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] 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 the condition [] and the second with the condition [].

WineQT_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

WineQT_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

WineQT_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] 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 the condition [] and the second with the condition [].

loan_data_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

loan_data_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

loan_data_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] 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 the condition [] and the second with the condition [].

Dry_Bean_Dataset_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

Dry_Bean_Dataset_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

Dry_Bean_Dataset_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] 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 the condition [] and the second with the condition [].

credit_customers_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

credit_customers_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

credit_customers_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] 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 the condition [] and the second with the condition [].

weatherAUS_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

weatherAUS_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

weatherAUS_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] 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 the condition [] and the second with the condition [].

car_insurance_overfitting_mlp.png;A multi-line chart showing the overfitting of a mlp where the y-axis represents the accuracy and the x-axis represents the number of iterations ranging from 100 to 1000.

car_insurance_overfitting_gb.png;A multi-line chart showing the overfitting of gradient boosting where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

car_insurance_overfitting_rf.png;A multi-line chart showing the overfitting of random forest where the y-axis represents the accuracy and the x-axis represents the number of estimators ranging from 2 to 2002.

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 [] principal components.