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How to print the accuracy in each fold of a Validation Dataset ? And how can I assign the fold number to each row in the dataframe?
classifier = RandomForestRegressor(n_jobs = -1, criterion='mse')
clf = GridSearchCV(classifier, param_grid = tunedParameters,cv=10)
all_accuracies = cross_val_score(classifier, X=X_train, y=y_train, cv=10)
print(all_accuracies)
You're mixing up GridSearchCV and cross_val_score; you should only need to run one of them.
GridSearchCV will search through your hyperparameter space, for each combination using cross-validation and producing a score. You can access these scores through the attribute cv_results_.
cross_val_score has no hyperparameter search; it just scores using cross-validation. The output is a list of the individual fold scores.
If you've already used GridSearchCV, there's probably no reason to use cross_val_score. (After hyperparameter searching, you've seen and used all the data in that set, so the scores in cv_results_ are biased as would be the scores out of cross_val_score; if you need an unbiased estimate of performance, you'll need another test set (or nested cross-validation in the first place).)
If you want to keep track of which samples go in which fold, I think you need to use a cross-validation generator or iterable for the cv parameter instead of an integer. Then you can use that generator/iterable to also tell you which samples are in which fold.
