In the context of model evaluation, explain the concept of cross-validation and its benefits over a simple train-validation split. Provide a scenario where you would choose cross-validation over a train-validation split and justify your choice.

Simulated

Cross-validation is a method of splitting the dataset into multiple subsets, training the model on some subsets and validating on the remaining subset. It is beneficial when the dataset is small and we want to make the most out of the available data.

9.1%

Cross-validation is a technique where the model is trained and validated on the same subset of the dataset. It is beneficial when the dataset is large and we want to ensure that the model is not overfitting on the training data.

9.1%

Cross-validation is a method of training the model on the entire dataset and validating it on a separate test set. It is beneficial when we want to ensure that the model generalizes well to unseen data.

4.5%

Cross-validation is a technique where the model is trained on a subset of the dataset and validated on the remaining subset multiple times, with different subsets used for validation each time. It is beneficial when we want to get a more robust estimate of the model's performance.

77.3%

Databricks Certified Machine Learning - Associate

Get started today

Comments

In the context of model evaluation, explain the concept of cross-validation and its benefits over a simple train-validation split. Provide a scenario where you would choose cross-validation over a train-validation split and justify your choice.