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Glossary · Machine Learning

What is Cross-Validation?

Evaluating a model by training and validating it several times on different splits of the data and averaging the results.

In more detail

K-fold cross-validation gives a more reliable estimate than one split and shows how stable a model is. Used to compare and tune models.

Example

5-fold CV F1: 0.94 ± 0.02

Learn it properlyModel Evaluation Metrics: Confusion Matrix, Precision, Recall, F1 and Cross-Validation12 min lesson · Machine Learning

Related terms

Data LeakageWhen information unavailable at prediction time sneaks into training, making a model look better than it really is.Feature EngineeringCreating, transforming and selecting input variables so a model can learn patterns more easily.Linear RegressionA model that predicts a number as a weighted sum of input features — a best-fit straight line or plane.Precision and RecallTwo classification metrics: precision is how many flagged items were correct; recall is how many actual positives were caught.Random ForestAn ensemble model that combines many decision trees trained on random samples of data and features.Train-Test SplitDividing data into a part for training a model and a held-out part for checking how it performs on unseen data.