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  3. Extreme Gradient Boosting with XGBoost

Exercise

Random search with XGBoost

Often, GridSearchCV can be really time consuming, so in practice, you may want to use RandomizedSearchCV instead, as you will do in this exercise. The good news is you only have to make a few modifications to your GridSearchCV code to do RandomizedSearchCV. The key difference is you have to specify a param_distributions parameter instead of a param_grid parameter.

Instructions

100 XP
  • Create a parameter grid called gbm_param_grid that contains a list with a single value for 'n_estimators' (25), and a list of 'max_depth' values between 2 and 11 for 'max_depth' - use range(2, 12) for this.
  • Create a RandomizedSearchCV object called randomized_mse, passing in: the parameter grid to param_distributions, the XGBRegressor to estimator, "neg_mean_squared_error" to scoring, 5 to n_iter, and 4 to cv. Also specify verbose=1 so you can better understand the output.
  • Fit the RandomizedSearchCV object to X and y.