What is cross-validation in machine learning?

Cross-validation is a technique used to check how well a machine learning model will perform on new data.

How It Works

• The dataset is divided into several smaller parts (called folds).
• The model is trained on some folds and tested on the remaining fold.
• This process is repeated until every fold has been used for testing.

Why Is It Important?

1. Gives a more reliable measure of model performance.
2. Reduces the risk of overfitting.
3. Makes better use of available data.

Simple Example

Suppose you have 100 student records.

• Split them into 5 groups of 20 records each.
• Train the model on 80 records and test on 20.
• Repeat this 5 times with a different test group each time.

In short: Cross-validation helps ensure that a machine learning model works well on unseen data, not just on the data it was trained on.