What is the purpose of the k-fold cross-validation technique in machine learning?a.To evaluate a model's performance on a separate test dataset.b.To reduce the risk of overfitting by training and testing a model on different data subsets.c.To speed up the training process by using parallel computing.d.To partition the dataset into k equal subsets for training and testing.

What is the purpose of cross-validation in machine learning?(1 Point)To evaluate the performance of a model on a held-out test setTo evaluate the performance of a model on different subsets of the dataTo compare the performance of different modelsTo tune the hyperparameters of a model

In K-Fold Cross-Validation, what does 'K' stand for?Review LaterThe number of times the entire procedure is repeatedThe number of folds the data is split intoThe number of parameters in the modelThe number of validation sets used

What is the purpose of evaluating the model's performance in each iteration of K-Fold Cross-Validation?Review LaterTo determine the optimal hyperparameters for the modelTo measure the model's accuracy on the training dataTo assess how well the model generalizes to unseen dataTo compute the average score for the model

Cross-validation is used to: Test a model on new data Train a model on multiple datasets Evaluate model performance on a held-out test set Simulate the training process

Explain the concept of cross-validation. Why is it used, and what are some common cross-validation techniques? (To Answer - speak your choice loudly and then logically explain your choice.)