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How does nested CV work for model selection?
From what I read online, nested CV works as follows: There is the inner CV loop, where we may conduct a grid search (e.g. running K-fold for every available model, e.g. combination of hyperparameters/features) There is the outer CV loop, where we measure the performance of the model that won in the inner fold, on a separate external fold.
When to use nested cross validation instead of nesting?
Update (2019): the more I think about these situations, the more I come to favor the “nested cross validation apparently without nesting” approach. In addition to cebeleites excellent answer (+1), the basic idea is that cross-validation is used to assess the performance of a method for fitting a model, not of the model itself.
What are the members of gridsearchcv model selection?
Exhaustive search over specified parameter values for an estimator. Important members are fit, predict. GridSearchCV implements a “fit” and a “score” method. It also implements “score_samples”, “predict”, “predict_proba”, “decision_function”, “transform” and “inverse_transform” if they are implemented in the estimator used.
Is it good practice to use gridsearch for cross validation?
Usually GridSearch has CV built in and takes a parameter on how many folds we wish to test. Combining those two I think its a good practice but the model from GridSearch and CrossValidation is not your final model.
How is cross validation used to estimate prediction error?
Abstract Cross-validation is a widely-used technique to estimate prediction error, but its behavior is complex and not fully understood. Ideally, one would like to think that cross-validation estimates the prediction error for the model at hand, t to the training data.
How is model validation related to the outer CV loop?
First of all, detecting in the outer CV loop that the models do not yield stable predictions in that respect doesn’t really differ from detecting that the prediciton error is too high for the application. It is one of the possible outcomes of model validation (or verification) implying that the model we have is not fit for its purpose.
When do you need nested cross validation for model selection?
If you need to perform model selection, then you need to perform that independently in each fold of the cross-validation procedure, as it is an integral part of the model fitting procedure. If you use a cross-validation based model selection procedure, this means you end up with nested cross-validation.