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.

What is the main characteristic of Shuffle Split Cross-Validation?Review LaterIt preserves the class distribution within each foldIt uses historical data for training and recent data for validationIt creates random train/validation splits with controlled proportionsIt ensures that samples belonging to the same group are kept together

K-Fold Cross-Validation splits the data into K equal-sized folds and trains the model K timesReview 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

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 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.