Overfitting
Overfitting is a common training error where a model learns the details and noise in the training dataset to the extent that it negatively impacts its performance on new, unseen test data. The model performs exceptionally well on training data but fails to generalize.
Frequently Asked Questions
How do you prevent overfitting?▼
By using techniques like regularization (L1/L2), dropout, early stopping, cross-validation, and adding more training data.
What is underfitting?▼
Underfitting is the opposite; when the model is too simple to learn the relationships in the data, performing poorly on both training and test sets.
Quick Facts
- CategoryModel Training
- Key ApplicationValidation curve checking, early stopping, and regularized training adjustments
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