cv_score_av = round(
np.mean(cross_val_score(ml_10_svm, x_10, y_10, cv=skf)) * 100, 1)
print('Cross-Validation Accuracy Score ML10: ', cv_score_av, '%\n')
cv_score_av = round(
np.mean(cross_val_score(ml_5_svm, x_5, y_5, cv=skf)) * 100, 1)
print('Cross-Validation Accuracy Score ML5: ', cv_score_av, '%\n')
# ---------- PREDICTION PROBABILITY PLOTS ----------
if pred_prob_plot_df10:
fig = pred_proba_plot(ml_10_svm,
x_10,
y_10,
no_iter=50,
no_bins=35,
x_min=0.3,
classifier='Support Vector Machine (ml_10)')
if save_pred_prob_plot_df10:
fig.savefig('figures/ml_10_svm_pred_proba.png')
if pred_prob_plot_df5:
fig = pred_proba_plot(ml_5_svm,
x_5,
y_5,
no_iter=50,
no_bins=35,
x_min=0.3,
classifier='Support Vector Machine (ml_5)')
if save_pred_prob_plot_df5:
cv_score_av = round(
np.mean(cross_val_score(ml_10_rand_forest, x_10, y_10, cv=skf)) * 100, 1)
print('Cross-Validation Accuracy Score ML10: ', cv_score_av, '%\n')
cv_score_av = round(
np.mean(cross_val_score(ml_5_rand_forest, x_5, y_5, cv=skf)) * 100, 1)
print('Cross-Validation Accuracy Score ML5: ', cv_score_av, '%\n')
# ---------- PREDICTION PROBABILITY PLOTS ----------
if pred_prob_plot_df10:
fig = pred_proba_plot(ml_10_rand_forest,
x_10,
y_10,
no_iter=50,
no_bins=36,
x_min=0.3,
classifier='Random Forest (ml_10)')
if save_pred_prob_plot_df10:
fig.savefig('figures/ml_10_random_forest_pred_proba.png')
if pred_prob_plot_df5:
fig = pred_proba_plot(ml_5_rand_forest,
x_5,
y_5,
no_iter=50,
no_bins=35,
x_min=0.3,
classifier='Random Forest (ml_5)')
if save_pred_prob_plot_df5:
cv_score_av = round(
np.mean(cross_val_score(ml_10_knn, x_10, y_10, cv=skf)) * 100, 1)
print('Cross-Validation Accuracy Score ML10: ', cv_score_av, '%\n')
cv_score_av = round(
np.mean(cross_val_score(ml_5_knn, x_5, y_5, cv=skf)) * 100, 1)
print('Cross-Validation Accuracy Score ML5: ', cv_score_av, '%\n')
# ---------- PREDICTION PROBABILITY PLOTS ----------
if pred_prob_plot_df10:
fig = pred_proba_plot(ml_10_knn,
x_10,
y_10,
no_iter=50,
no_bins=18,
x_min=0.3,
classifier='Nearest Neighbor (ml_10)')
if save_pred_prob_plot_df10:
fig.savefig('figures/ml_10_nearest_neighbor_pred_proba.png')
if pred_prob_plot_df5:
fig = pred_proba_plot(ml_5_knn,
x_5,
y_5,
no_iter=50,
no_bins=18,
x_min=0.3,
classifier='Nearest Neighbor (ml_5)')
if save_pred_prob_plot_df10: