thinQmaster Virtual testing partner Adaptive practice exams that find your weak spots and drill them. Every answer explained. From $69
Enroll

thinQmaster

Find your exam

Five questions free, before you decide.

Step 1 · choose a vendor

In one of our camps? Your practice exams come with the tuition — they are on your study page already.

AI & Machine Learning · Intermediate

Cross-Validation

A resampling method that trains and evaluates a model on multiple different splits to estimate generalization more reliably.

Definition

In k-fold cross-validation the data is divided into k folds; the model trains on k-1 folds and validates on the remaining one, rotating k times and averaging the scores. It makes efficient use of limited data and reduces variance in performance estimates. It costs k times the training compute.

Example

5-fold cross-validation trains five models, each validated on a different 20% of the data.

Exam tip

Use cross-validation when data is limited; use time-series split for temporal data.

Related terms