eduvedras/CaptionTemplatesDataset · Datasets at Hugging Face

Chart;description stringlengths 56 243
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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] 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 the condition [] and the second with the condition [].
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 [] principal components.
Iris_correlation_heatmap.png;A heatmap showing the correlation between the variables of the dataset. The variables are [].

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

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

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

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

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

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

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

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

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 [] principal components.

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