Definition
Cross-validation is a statistical method used to estimate the skill of machine learning models. The most common form is k-fold cross-validation, where the data is split into k equal parts. The model is trained on k-1 folds and validated on the remaining fold, repeating this process k times so each fold serves as the validation set once. This approach provides a more robust estimate of model performance than a single train-test split, helping to detect overfitting and ensuring the model generalizes well to unseen data.
Summary
A resampling procedure used to evaluate machine learning models on a limited data sample by partitioning data into subsets for training and testing.
Key Concepts
- K-Fold Split
- Model Generalization
- Overfitting Detection
- Performance Estimation
Use Cases
- Hyperparameter tuning
- Comparing different algorithms
- Validating model stability on small datasets
Code Example
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