RandomSearchCV in Scikit Learn

Let's practice building a RandomizedSearchCV object using Scikit Learn. Instead this time we will use a different algorithm and some different instructions.

The desired options are:

A RandomForestClassifier Estimator with default 80 estimators
3-fold cross validation
Use AUC to score the models
Use 4 cores for processing in parallel
Ensure you refit the best model and return training scores
Randomly sample 5 models for processing efficiency

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

You will have available X_train & y_train datasets.

Create a parameter 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.
Print the values chosen by the modeling process for both hyperparameters.

Take Hint (-30 XP)

Exercise

RandomSearchCV in Scikit Learn

Instructions