The model hyperparameters get tuned using training and validation set. They are training, validation and test split.
Finally, the hyperparameters which result in most optimal mean and standard value of model scores get selected.Step 3 to Step 7 is repeated for different values of hyperparameters.Finally, the mean and standard deviation of the model performance is computed by taking all of the model scores calculated in step 5 for each of the K models.This is why it is called k-fold cross validation. The above steps (step 3, step 4 and step 5) is repeated until each of the k-fold got used for validation purpose.The performance of the model is recorded. The model with specific hyperparameters is trained with training data (K-1 folds) and validation data as 1 fold.
Out of the K-folds, (K-1) fold is used for training.The training dataset is then split into K-folds.The dataset is split into training and test dataset.The following is done in this technique for training, validating and testing the model: By using k-fold cross-validation, we are able to “test” the model on k different data sets, which helps to ensure that the model is generalizable. This yields a lower-variance estimate of the model performance than the holdout method. This technique is used because it helps to avoid overfitting, which can occur when a model is trained using all of the data. The advantage of this approach is that each example is used for training and validation (as part of a test fold) exactly once. It is a resampling technique without replacement. It is a technique used for hyperparameter tuning such that the model with the most optimal value of hyperparameters can be trained. K-fold cross-validation is defined as a method for estimating the performance of a model on unseen data. What are disadvantages of k-fold cross validation?.K-fold Cross-Validation with Python (using Sklearn.cross_val_score).K-fold Cross-Validation with Python (using Cross-Validation Generators).What and Why of K-fold Cross Validation.