How do I perform k-fold cross-validation for hyperparameter optimization in XGBoost

Question

With the code can you tell me How do I perform k-fold cross-validation for hyperparameter optimization in XGBoost?

Answer 1

To perform k-fold cross-validation for hyperparameter optimization in XGBoost, use XGBClassifier or XGBRegressor with GridSearchCV or RandomizedSearchCV from sklearn.model_selection, ensuring optimal parameters are found via cross-validation.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Uses GridSearchCV for Exhaustive Hyperparameter Search:

  • Tunes n_estimators, max_depth, learning_rate, subsample, colsample_bytree.

• Implements StratifiedKFold for Balanced Class Distribution:

  • Ensures each fold maintains class proportions, avoiding bias.

• Automatically Selects Best Model (grid_search.best_estimator_):

  • Uses 5-fold CV to find the best hyperparameter combination.

Hence, using k-fold cross-validation in GridSearchCV ensures optimal hyperparameter selection in XGBoost while preventing overfitting, leading to better generalization.

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