How does overfitting affect the performance of a machine learning model? Pending Review
Asked on Nov 15, 2025
Answer
Overfitting occurs when a machine learning model learns the training data too well, capturing noise and details that do not generalize to new data, leading to poor performance on unseen data.
Example Concept: Overfitting happens when a model is too complex, such as having too many parameters relative to the number of observations. This complexity allows the model to fit the training data almost perfectly, including any noise or outliers, but it fails to predict new data accurately because it hasn't learned the underlying patterns. As a result, the model performs well on training data but poorly on validation or test data.
Additional Comment:
- Overfitting is often identified by a large gap between training and validation/test accuracy.
- Common strategies to prevent overfitting include using simpler models, regularization techniques, and cross-validation.
- Data augmentation and increasing the size of the training dataset can also help mitigate overfitting.
- Monitoring model performance on a separate validation set during training helps detect overfitting early.
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