How do I implement cross-validation in my machine learning model?

How do I implement cross-validation in my machine learning model?

Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. Would you like to know about different types of cross-validation?

Yes, what are the different types?

The most common types are k-fold cross-validation, stratified k-fold, and leave-one-out cross-validation. K-fold is the most widely used.

How does k-fold cross-validation work?

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. This process is repeated 'k' times.

What are the benefits of using cross-validation?

Cross-validation helps in reducing overfitting, provides a better estimate of model performance, and ensures that every data point gets to be in both training and testing sets.