What is k-fold cross-validation and how does it work?

What is k-fold cross-validation and how does it work?

K-fold cross-validation is a technique used to assess the performance of a model by dividing the dataset into 'k' subsets. The model is trained on 'k-1' subsets and tested on the remaining subset. This process is repeated 'k' times, with each subset used as the test set once.

Why is k-fold cross-validation preferred over a simple train-test split?

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

How do I choose the value of k?

The value of k is often chosen based on the size of the dataset. A common choice is 5 or 10, but for smaller datasets, a higher k can be used to ensure that the model is trained on a larger portion of the data.

Can you give me an example of how to implement k-fold cross-validation in Python?

Sure! You can use the KFold class from the sklearn.model_selection module. Here's a simple example:

from sklearn.model_selection import KFold

kf = KFold(n_splits=5)
for train_index, test_index in kf.split(X):
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
    # Train your model here