Plotting an ROC curve

Great job in the previous exercise - you now have a new addition to your toolbox of classifiers!

Classification reports and confusion matrices are great methods to quantitatively evaluate model performance, while ROC curves provide a way to visually evaluate models. As Hugo demonstrated in the video, most classifiers in scikit-learn have a .predict_proba() method which returns the probability of a given sample being in a particular class. Having built a logistic regression model, you'll now evaluate its performance by plotting an ROC curve. In doing so, you'll make use of the .predict_proba() method and become familiar with its functionality.

Here, you'll continue working with the PIMA Indians diabetes dataset. The classifier has already been fit to the training data and is available as logreg.

Import roc_curve from sklearn.metrics.
Using the logreg classifier, which has been fit to the training data, compute the predicted probabilities of the labels of the test set X_test. Save the result as y_pred_prob.
Use the roc_curve() function with y_test and y_pred_prob and unpack the result into the variables fpr, tpr, and thresholds.
Plot the ROC curve with fpr on the x-axis and tpr on the y-axis.

Exercise

Plotting an ROC curve

Instructions