<jupyter_start><jupyter_text>Train performance<jupyter_code>print('Confusion Matrix:', confusion_matrix(y_train_pred,y_train))
print('Accuracy Score:', accuracy_score(y_train_pred,y_train))
print('Precision Score:', precision_score(y_train_pred,y_train))
print('Recall Score:', recall_score(y_train_pred,y_train))<jupyter_output>Accuracy Score: 0.946875
Precision Score: 0.9370629370629371
Recall Score: 0.9436619718309859<jupyter_text>Test performance<jupyter_code>print('Confusion Matrix:', confusion_matrix(y_test_pred,y_test))
print('Accuracy Score:', accuracy_score(y_test_pred,y_test))
print('Precision Score:', precision_score(y_test_pred,y_test))
print('Recall Score:', recall_score(y_test_pred,y_test))
from sklearn.metrics import roc_curve
from sklearn.metrics import auc
y_pred_proba = rf_model.predict_proba(X_test)[:,1]
fpr,tpr,th = roc_curve(y_test,y_pred_proba)
plt.plot(fpr, tpr)
print("AUC Score:",auc(fpr,tpr))<jupyter_output>AUC Score: 0.9010056568196103
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