How to tune hyperparameters of multiple models with GridSearchCV in scikit-learn?

I’m diving into hyperparameter tuning for ensemble models, and I’ve heard about a technique called “grid searching over multiple models.” However, I’m not entirely sure how to implement it effectively. Could you provide some insights into how to use GridSearchCV for tuning hyperparameters in multiple models simultaneously? Additionally, it would be helpful to see a code example demonstrating this approach in practice.

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It is possible to use GridSearchCV to tune hyperparameters for multiple models simultaneously. This approach is called “grid searching over multiple models,” and it involves creating a pipeline that combines multiple models with different hyperparameters, and then using GridSearchCV to search over the pipeline’s hyperparameters.

If you want to learn more about GridSearchCV, you can check out this thread Using parallel processing in scikit learn to speed up GridSearchCV

Here is an example of how to use GridSearchCV to tune hyperparameters for multiple models simultaneously:

This code performs hyperparameter tuning using GridSearchCV on a pipeline that combines two models, SVC and RandomForestClassifier, on the Iris dataset. The hyperparameters for each model are defined in a dictionary, and the ‘accuracy’ scoring metric is used to evaluate the models. The best hyperparameters for each model are identified by fitting the pipeline to the data using cross-validation.

I hope this aligns well with your query!