The inner loop of nested cross-validation performs model training and hyperparameter tuning on the same set of folds used for model evaluation.Review LaterTrueFalse

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

Question 8Which of the following statements about cross-validation is/are True?1 pointCross-validation is essential step in hyperparameter tuning.We can manually generate folds by using KFold function.GridSearchCV is commontly used in cross-validation.All of the above are True.

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

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

What is the impact of using a small number of folds in cross-validation?Review LaterIt leads to overfitting and high variance.It results in underfitting and high bias.It provides stable performance estimates.It allows the model to capture complex patterns.