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  3. Hyperparameter Tuning in Python

Exercise

Using the best results

While it is interesting to analyze the results of our grid search, our final goal is practical in nature; we want to make predictions on our test set using our estimator object.

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

Let's take a look inside the best_estimator_ property, make predictions, and generate evaluation scores. We will firstly use the default predict (giving class predictions), but then we will need to use predict_proba rather than predict to generate the roc-auc score as roc-auc needs probability scores for its calculation. We use a slice [:,1] to get probabilities of the positive class.

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

Instructions

100 XP
  • 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.