Evaluate the optimal tree

In this exercise, you'll evaluate the test set ROC AUC score of grid_dt's optimal model.

In order to do so, you will first determine the probability of obtaining the positive label for each test set observation. You can use the methodpredict_proba() of an sklearn classifier to compute a 2D array containing the probabilities of the negative and positive class-labels respectively along columns.

The dataset is already loaded and processed for you (numerical features are standardized); it is split into 80% train and 20% test. X_test, y_test are available in your workspace. In addition, we have also loaded the trained GridSearchCV object grid_dt that you instantiated in the previous exercise. Note that grid_dt was trained as follows:

grid_dt.fit(X_train, y_train)

Import roc_auc_score from sklearn.metrics.
Extract the .best_estimator_ attribute from grid_dt and assign it to best_model.
Predict the test set probabilities of obtaining the positive class y_pred_proba.
Compute the test set ROC AUC score test_roc_auc of best_model.

Exercise

Evaluate the optimal tree

Instructions