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  3. Hyperparameter Tuning in Python

Exercise

Exploring the grid search results

You will now explore the cv_results_ property of the GridSearchCV object defined in the video. This is a dictionary that we can read into a pandas DataFrame and contains a lot of useful information about the grid search we just undertook.

A reminder of the different column types in this property:

  • time_ columns
  • param_ columns (one for each hyperparameter) and the singular params column (with all hyperparameter settings)
  • a train_score column for each cv fold including the mean_train_score and std_train_score columns
  • a test_score column for each cv fold including the mean_test_score and std_test_score columns
  • a rank_test_score column with a number from 1 to n (number of iterations) ranking the rows based on their mean_test_score

Instructions

100 XP
  • Read the cv_results_ property of the grid_rf_class GridSearchCV object into a data frame & print the whole thing out to inspect.
  • Extract & print the singular column containing a dictionary of all hyperparameters used in each iteration of the grid search.
  • Extract & print the row that had the best mean test score by indexing using the rank_test_score column.