How do you evaluate the AUC-ROC curve for a binary classification model in Scikit-learn

Question

Can you tell me How do you evaluate the AUC-ROC curve for a binary classification model in Scikit-learn?

Answer 1

You can evaluate the AUC-ROC curve in Scikit-learn using roc_auc_score(y_true, y_prob) for binary classification.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• roc_auc_score(y_test, y_prob) calculates the AUC-ROC score, a single value representing model performance.
• roc_curve(y_test, y_prob) provides the data to plot the ROC curve.
• predict_proba() gets the probability estimates needed for the ROC and AUC calculations.
• The ROC curve visualizes the trade-off between the true positive rate and false positive rate.

Hence, the AUC-ROC score gives a comprehensive measure of model performance for binary classification, with higher values indicating better discriminatory ability. The ROC curve visually demonstrates this performance.