Detecting overfitting involves several strategies: - Cross-validation: Splitting the data into training and testing sets to evaluate the model’s performance on unseen data. - Performance Metrics: Comparing metrics such as Mean Squared Error (MSE) or Area Under the ROC Curve (AUC) on both training and testing datasets. - Complexity Penalties: Models with excessive parameters or high complexity are more prone to overfitting. Techniques like regularization can help mitigate this risk.
