Can you explain how GridSearchCV works in machine learning?
Sure! GridSearchCV is a technique used to find the best hyperparameters for a model by performing an exhaustive search over specified parameter values.
How does it determine the best parameters?
It evaluates all possible combinations of the provided hyperparameters using cross-validation, scoring each combination to find the one with the best performance.
What are some common parameters I can tune with GridSearchCV?
Common parameters include the number of estimators, learning rate, max depth for trees, and regularization parameters, depending on the model.
Are there any drawbacks to using GridSearchCV?
Yes, it can be computationally expensive, especially with large datasets or many hyperparameters, as it evaluates every combination.
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