In this video, we'll learn about K-fold cross-validation and how it can be used for selecting optimal tuning parameters, choosing between models, and selecting features. We'll compare cross-validation with the train/test split procedure, and we'll also discuss some variations of cross-validation that can result in more accurate estimates of model performance.
Download the notebook: [ Ссылка ]
Documentation on cross-validation: [ Ссылка ]
Documentation on model evaluation: [ Ссылка ]
GitHub issue on negative mean squared error: [ Ссылка ]
An Introduction to Statistical Learning: [ Ссылка ]
K-fold and leave-one-out cross-validation: [ Ссылка ]
Cross-validation the right and wrong ways: [ Ссылка ]
Accurately Measuring Model Prediction Error: [ Ссылка ]
An Introduction to Feature Selection: [ Ссылка ]
Harvard CS109: [ Ссылка ]
Cross-validation pitfalls: [ Ссылка ]
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