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Understanding Generalization Error in ML by Tusshu Bisane is a document available to read on EtoBox.
What is Understanding Generalization Error in ML about?
Generalization error measures the difference in performance of a learning algorithm on unseen data compared to training data, indicating its ability to adapt and avoid overfitting. It can be estimated using validation sets, reduced through techniques like regularization and cross-validation, and evaluated using metrics such as Mean Squared Error (MSE) and Mean Absolute Error (MAE). Understanding generalization error is crucial for improving algorithm performance and achieving accurate predictions on new dat
- Author
- Tusshu Bisane
- Language
- EN