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Exercise

RandomSearchCV in Scikit Learn

Let's practice building a RandomizedSearchCV object using Scikit Learn.

The hyperparameter grid should be for max_depth (all values between and including 5 and 25) and max_features ('auto' and 'sqrt').

The desired options for the RandomizedSearchCV object are:

  • A RandomForestClassifier Estimator with n_estimators of 80.
  • 3-fold cross validation (cv)
  • Use roc_auc to score the models
  • Use 4 cores for processing in parallel (n_jobs)
  • Ensure you refit the best model and return training scores
  • Only sample 5 models for efficiency (n_iter)

X_train & y_train datasets are loaded for you.

Remember, to extract the chosen hyperparameters these are found in cv_results_ with a column per hyperparameter. For example, the column for the hyperparameter criterion would be param_criterion.

Instructions

100 XP
  • Create a hyperparameter grid as specified in the context above.
  • Create a RandomizedSearchCV object as outlined in the context above.
  • Fit the RandomizedSearchCV object to the training data.
  • Index into the cv_results_ object to print the values chosen by the modeling process for both hyperparameters (max_depth and max_features).