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model_object = SVC()

hyperparameters = {
"C":[1,10,20],
"kernel":["rbf","linear"]
}

grid = GridSearchCV(model_object,hyperparameters,cv=5,return_train_score=False)
grid.fit(x,y)

gridSearch_result = grid.cv_results_


Above I am trying to tune some hyperparameters using sklearn GridSearchCV() , and among's other parameter I am not getting what is the purpose of the parameter "cv".

I looked online and also in the documentation and it says "number of cross validations to perform" , but what does that mean ? I know K-Fold and other cross validation methods. Does cv means the value of K ?


What I have tried:

I tried looking online into other resources
Posted
Updated 15-May-22 2:02am

The documentation is the place to find the details: sklearn.model_selection.GridSearchCV — scikit-learn 0.24.1 documentation[^]
   
cross-validation: the score of each combination of parameters on the grid is computed by using an internal cross-validation procedure
   

This content, along with any associated source code and files, is licensed under The Code Project Open License (CPOL)

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