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  3. Extreme Gradient Boosting with XGBoost
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Evaluating model quality

It's now time to begin evaluating model quality.

Here, you will compare the RMSE and MAE of a cross-validated XGBoost model on the Ames housing data. As in previous exercises, all necessary modules have been pre-loaded and the data is available in the DataFrame df.

说明 1 / 共 2 个

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  • 1
    • Perform 4-fold cross-validation with 5 boosting rounds and "rmse" as the metric.
    • Extract and print the final boosting round RMSE.
  • 2

    Now, adapt your code to compute the "mae" instead of the "rmse".