Cross-Validation
Cross-Validation is a statistical resampling technique used to evaluate a machine learning model's generalization performance by partitioning the dataset into multiple training and validation folds and testing recursively.
Frequently Asked Questions
What is K-Fold Cross-Validation?▼
The most popular cross-validation method where the dataset is split into K equal-sized folds. The model is trained K times, each time using K-1 folds for training and the remaining single fold for validation, averaging the performance scores.
Why use cross-validation instead of a single train-test split?▼
A single split can lead to high variance in evaluation scores if the validation split happens to contain abnormally easy or hard examples. Cross-validation ensures every data point is used for validation exactly once, giving a robust, low-bias score.
Quick Facts
- CategoryModel Training
- Key ApplicationModel validation audits, hyperparameter tuning evaluations, and model selection.
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