Tuning the number of boosting rounds

Let's start with parameter tuning by seeing how the number of boosting rounds (number of trees you build) impacts the out-of-sample performance of your XGBoost model. You'll use xgb.cv() inside a for loop and build one model per num_boost_round parameter.

Here, you'll continue working with the Ames housing dataset. The features are available in the array X, and the target vector is contained in y.

Create a DMatrix called housing_dmatrix from X and y.
Create a parameter dictionary called params, passing in the appropriate "objective" ("reg:linear") and "max_depth" (set it to 3).
Iterate over num_rounds inside a for loop and perform 3-fold cross-validation. In each iteration of the loop, pass in the current number of boosting rounds (curr_num_rounds) to xgb.cv() as the argument to num_boost_round.
Append the final boosting round RMSE for each cross-validated XGBoost model to the final_rmse_per_round list.
num_rounds and final_rmse_per_round have been zipped and converted into a DataFrame so you can easily see how the model performs with each boosting round. Hit 'Submit Answer' to see the results!

Exercise

Tuning the number of boosting rounds

Instructions