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.
Diese Übung ist Teil des Kurses
Extreme Gradient Boosting with XGBoost
Interaktive Übung
Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.
# Create the DMatrix: housing_dmatrix
housing_dmatrix = xgb.DMatrix(data=X, label=y)
# Create the parameter dictionary: params
params = {"objective":"reg:squarederror", "max_depth":4}
# Perform cross-validation: cv_results
cv_results = ____(dtrain=____, params=____, nfold=____, num_boost_round=____, metrics=____, as_pandas=True, seed=123)
# Print cv_results
print(cv_results)
# Extract and print final boosting round metric
print((cv_results["____"]).tail(1))