What is the difference between cross-validation and train-test split?

What is the difference between cross-validation and train-test split?

Cross-validation is a technique to assess how a model will generalize to an independent dataset, while train-test split divides the dataset into two parts: one for training and one for testing.

Why is cross-validation preferred over train-test split?

Cross-validation provides a more reliable estimate of model performance by using multiple train-test splits, reducing the risk of overfitting.

Can you explain how k-fold cross-validation works?

In k-fold cross-validation, the dataset is divided into k subsets. The model is trained on k-1 subsets and tested on the remaining subset, repeating this process k times.

What are some common pitfalls of using train-test split?

Common pitfalls include overfitting to the training set, underestimating variance, and not utilizing all available data effectively.

When should I use cross-validation instead of train-test split?

Use cross-validation when you have a limited dataset and want to maximize the training data used for model evaluation.