Using the best results

Remember that our grid search object has been fit to our training data. Whilst it is interesting to analyze the results of our grid search, our final goal is practical in nature. We want to be able to use the best parameters to undertake prediction and use the scores on the test set as the true measure for how well our model generalizes to new data.

We can access this object through the best_estimator_ property of our grid search object.

In this exercise we will take a look inside the best_estimator_ property and then use this to make predictions on our test set for credit card defaults and generate a variety of scores.

You have available the X_test and y_test datasets to use and the grid_rf_class object from previous exercises.

Check the type of the best_estimator_ property.
Use the best_estimator_ property to make predictions on our test set.
Generate a confusion matrix and ROC_AUC score from our predictions.

Take Hint (-30 XP)

Exercise

Using the best results

Instructions