How do you use RandomizedSearchCV for hyperparameter tuning of a GradientBoostingClassifier in Scikit-learn

Question

Can i know How do you use RandomizedSearchCV for hyperparameter tuning of a GradientBoostingClassifier in Scikit-learn?

Answer 1

You can use RandomizedSearchCV in Scikit-learn to efficiently tune hyperparameters of a GradientBoostingClassifier by sampling a given parameter grid.

Here is the code snippet you can refer to:

In the above code we are using the following key points:

• RandomizedSearchCV() randomly samples hyperparameter combinations, reducing computation time compared to exhaustive search.
• param_dist defines a grid of possible hyperparameter values.
• n_iter=10 controls the number of sampled combinations.
• cv=5 applies 5-fold cross-validation to avoid overfitting.
• best_estimator_ and best_params_ return the optimal model and its parameters.

Hence, RandomizedSearchCV provides an efficient approach to hyperparameter tuning, balancing performance and computation time, especially for complex models like GradientBoostingClassifier.