Cross-Validation

Intermediate

A robust evaluation technique that trains/evaluates across multiple splits to estimate performance variability.

Full Definition

A robust evaluation technique that trains/evaluates across multiple splits to estimate performance variability.

Keywords

k-fold resampling

Domains

Foundations & Theory

Related Terms

Confusion Matrix related to

A table summarizing classification outcomes, foundational for metrics like precision, recall, specificity.

 Accuracy related to

Fraction of correct predictions; can be misleading on imbalanced datasets.

 Precision related to

Of predicted positives, the fraction that are truly positive; sensitive to false positives.

 Recall related to

Of true positives, the fraction correctly identified; sensitive to false negatives.

 Underfitting related to

When a model cannot capture underlying structure, performing poorly on both training and test data.

 Bias–Variance Tradeoff related to

A conceptual framework describing error as the sum of systematic error (bias) and sensitivity to data (variance).

 Generalization related to

How well a model performs on new data drawn from the same (or similar) distribution as training.

 Data Leakage related to

When information from evaluation data improperly influences training, inflating reported performance.

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