Tuning colsample_bytree

Now, it's time to tune "colsample_bytree". You've already seen this if you've ever worked with scikit-learn's RandomForestClassifier or RandomForestRegressor, where it just was called max_features. In both xgboost and sklearn, this parameter (although named differently) simply specifies the fraction of features to choose from at every split in a given tree. In xgboost, colsample_bytree must be specified as a float between 0 and 1.

Este ejercicio forma parte del curso

Extreme Gradient Boosting with XGBoost

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Instrucciones del ejercicio

Create a list called colsample_bytree_vals to store the values 0.1, 0.5, 0.8, and 1.
Systematically vary "colsample_bytree" and perform cross-validation, exactly as you did with max_depth and eta previously.

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# Create your housing DMatrix
housing_dmatrix = xgb.DMatrix(data=X,label=y)

# Create the parameter dictionary
params={"objective":"reg:squarederror","max_depth":3}

# Create list of hyperparameter values: colsample_bytree_vals
____ = ____
best_rmse = []

# Systematically vary the hyperparameter value 
for curr_val in ____:

    ____ = ____
    
    # Perform cross-validation
    cv_results = xgb.cv(dtrain=housing_dmatrix, params=params, nfold=2,
                 num_boost_round=10, early_stopping_rounds=5,
                 metrics="rmse", as_pandas=True, seed=123)
    
    # Append the final round rmse to best_rmse
    best_rmse.append(cv_results["test-rmse-mean"].tail().values[-1])

# Print the resultant DataFrame
print(pd.DataFrame(list(zip(colsample_bytree_vals, best_rmse)), columns=["colsample_bytree","best_rmse"]))

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