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. Let me know if you’d like me to refine or expand anything!