Overfitting occurs when a machine learning model learns the training data too closely, including noise and accidental patterns, instead of learning relationships that generalize to new data. As a result, the model may perform very well on training data but poorly on unseen examples.
Common ways to reduce overfitting include:
- More training data: A larger and more diverse dataset can help the model learn general patterns instead of memorizing individual examples.
- Data augmentation: Creating realistic variations of existing training examples can improve generalization, especially for images, audio, and text.
- Regularization: Techniques such as L1 and L2 regularization discourage overly complex model parameters.
- Dropout: Randomly disables some neurons during training, reducing excessive dependence on particular features.
- Early stopping: Stops training when validation performance stops improving, preventing the model from continuing to fit noise.
- Cross-validation: Evaluates the model across different subsets of data to provide a more reliable estimate of generalization.
- Simpler models: Reducing unnecessary model complexity can lower the risk of memorizing the training data.
A useful practical sign of overfitting is a large gap between training and validation performance. If training accuracy keeps increasing while validation performance stops improving or gets worse, the model may be learning details that do not generalize.
In simple terms, overfitting means the model has learned the training examples too specifically rather than learning patterns that work on new data.
