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

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 primary purpose of k-fold cross-validation in machine learning?<br /> A. 1. To evaluate the model's performance while maximizing data utilization <br />B. 2. To train the model on the entire dataset <br />C. 3. To test the model's performance on a separate validation set <br />D. 4. To minimize computational resources required for training the model

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

What is the primary purpose of a validation set in the context of model training?Select one:a. To test the model on unseen datab. To tune hyperparameters and evaluate model performance

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.)