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Data Science & ML

Cross-validation

/krɒs ˌvælɪˈdeɪʃən/

Definition

A technique splitting data into k folds and training/evaluating k times to get a robust performance estimate.

Example in context

"5-fold cross-validation gives five accuracy scores — we report the mean and standard deviation, not just one split."

Related terms

  • Overfitting — When an ML model learns training data too precisely — including noise — and fails to generalise to new examples.

Practice this term

Master Cross-validation in context by working through exercises in the Data Science & ML module. You'll see the term used in real engineering scenarios with multiple-choice, fill-in-the-blank, and matching drills.

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Frequently Asked Questions

What does "Cross-validation" mean?

A technique splitting data into k folds and training/evaluating k times to get a robust performance estimate.

How do you pronounce "Cross-validation"?

"Cross-validation" is pronounced /krɒs ˌvælɪˈdeɪʃən/.

Can you use "Cross-validation" in a sentence?

"5-fold cross-validation gives five accuracy scores — we report the mean and standard deviation, not just one split."