gboost_model_evaluation.py

# train the model on the training set
gboost.fit(X_train, y_train)

# make class predictions for the testing set
y_pred_class = gboost.predict(X_test)

# IMPORTANT: first argument is true values, second argument is predicted values
print(metrics.confusion_matrix(y_test, y_pred_class))

binary = np.array([[125, 14],
                   [ 22, 62]])

fig, ax = plot_confusion_matrix(conf_mat=binary)
plt.show();

# Determine the false positive and true positive rates
fpr, tpr, _ = roc_curve(y_test, gboost.predict_proba(X_test)[:,1])
 
# Calculate the AUC
roc_auc = auc(fpr, tpr)
print('ROC AUC: %0.2f' % roc_auc)
 
# Plot of a ROC curve for a specific class
plt.figure()
plt.plot(fpr, tpr, label='ROC curve (area = %0.2f)' % roc_auc)
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('ROC Curve')
plt.legend(loc="lower right")
plt.show();