There are several techniques to detect overfitting:
1. Cross-Validation: A common method is to use cross-validation, where the data is divided into training and testing sets multiple times to ensure the model performs consistently well on unseen data. 2. Validation Curves: Plotting training and validation performance can help identify overfitting. A large gap between training and validation performance indicates overfitting. 3. Learning Curves: Learning curves can show if increasing the training data reduces the gap between training and validation performance, suggesting overfitting initially.
