{"version":"1.0","type":"rich","provider_name":"gaks.ai AI Glossary","provider_url":"https://gaks.ai/glossary","title":"Cross-Validation — AI Glossary","author_name":"Glenn Katrud Solheim","author_url":"https://gaks.ai","width":600,"height":200,"html":"

AI Glossary — gaks.ai

Cross-Validation

A technique for evaluating a model's performance by training and testing it on different subsets of the data multiple times. The most common approach is k-fold cross-validation, where the data is split into k subsets and the model is trained k times, each time using a different subset as the test set. It gives a more reliable estimate of how well the model will generalize to new data than a single train-test split. See also: overfitting, benchmark, Evaluation.

Source: gaks.ai/glossary/cross-validation →
"}