roc curve sklearn

from sklearn.svm import SVC

# Train the support vector machine classifier
svm = SVC(kernel='rbf', probability=True, random_state=42)
svm.fit(X_train_scaled, y_train)

# Calculate the predicted probabilities
y_pred_proba = svm.predict_proba(X_test_scaled)[:, 1]

# Compute the ROC curve
fpr, tpr, thresholds = roc_curve(y_test, y_pred_proba)
roc_auc = auc(fpr, tpr)

              
# Plot the ROC curve 
plt.figure()
plt.plot(fpr, tpr, color='darkorange', lw=2, label=f'ROC curve (area = {roc_auc:.2f})')
plt.plot([0, 1], [0, 1], color='navy', lw=2, linestyle='--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
plt.title('Receiver Operating Characteristic (ROC) Curve')
plt.legend(loc="lower right")
plt.show()