How do I evaluate model performance using k-fold cross-validation?

How do I evaluate model performance using k-fold cross-validation?

K-fold cross-validation is a technique to assess how a model performs on different subsets of data. You split your dataset into 'k' parts, train the model on 'k-1' parts, and validate it on the remaining part. This process is repeated 'k' times, with each part used as the validation set once.

What are the advantages of using k-fold over a simple train-test split?

K-fold cross-validation provides a more reliable estimate of model performance by using multiple train-test splits. It reduces the variance associated with a single train-test split and ensures that every data point gets to be in both training and validation sets.

How do I choose the value of k?

The value of k is often chosen based on the size of your dataset. A common choice is 5 or 10, but for smaller datasets, you might use a higher k to ensure that each training set is large enough.

Can k-fold cross-validation be used for all types of models?

Yes, k-fold cross-validation can be applied to any model, whether it's linear regression, decision trees, or neural networks. However, the computational cost may vary depending on the model complexity.