What is cross-validation and why is it used in ML?
What is cross-validation and why is it used in ML?
Ask by ICSM Computer
Updated 16 Apr 2025
Cross-validation shows how well machine learning models can predict new data by dividing the dataset into multiple parts and testing them. The method tests if our model works correctly on new data sets besides just the data it learned from. Using this approach helps create models that avoid both excessive and insufficient adjustments.
The dataset divides into different smaller parts or folds for cross-validation. K-fold cross-validation employs an equal split of data into specific numbers of parts. During training the model needs k-1 subsets while testing is performed on one subset. The test set receives one fold each during k consecutive runs through the data splitting process. We calculate the final evaluation result through averaging of all K measurements to arrive at a dependable performance estimate for our model.
People use cross-validation for these specific purposes:
Cross-validation helps developers lower their models' vulnerability to overfitting and creates effective designs for practical applications.