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Do we still need to do a train-test split before using k-fold cross-validation?
EDIT: For doing k-fold cross-validation, you don’t need to split the data into training and validation set, it is done by splitting the training data into k-folds, each one of which will be used as a validation set in training the other (k-1) folds together as training set.
What is difference between cross-validation and grid search?
Cross-validation is a method for robustly estimating test-set performance (generalization) of a model. Grid-search is a way to select the best of a family of models, parametrized by a grid of parameters.
Which is better grid search or kfold cross validation?
Grid Search is used to choose best combination of Hyper parameters of predictive algorithms (Tuning the hyper-parameters of an estimator) whereas KFold Provides train/test indices to split data in train/test sets. It Split dataset into k consecutive folds (without shuffling by default).
How is k-fold cross validation used in Python?
The process of K-Fold Cross-Validation is straightforward. You divide the data into K folds. Out of the K folds, K-1 sets are used for training while the remaining set is used for testing. The algorithm is trained and tested K times, each time a new set is used as testing set while remaining sets are used for training.
Is it a violation to use gamma in k-fold cross validation?
If I use the gamma and C of the first holdout and use it for the rest of 9 holdouts of the k-fold cross-validation, is that a violation because I would have used the train data to get gamma and C and again use the portion of train data as test in second holdout ?
When to use kfold CV and gridsearchcv?
Also if i understand correctly GridSearchCV is used for hyperparameter tuning i.e. what values of the arguments will give best result and the Kfold CV is used to better the generalization so that we are training like on different folds and hence reducing bias if the data is like ordered in some particular way and hence increasing generalization.