Several techniques can help identify overfitting in toxicological models:
Cross-validation: Splitting data into training and validation sets allows the model's performance to be tested on unseen data, offering insights into its generalization capabilities. Training vs. Validation Error: A significant gap between training and validation error rates typically indicates overfitting. Complexity Analysis: Assessing the complexity of the model, such as the number of parameters relative to the size of the dataset, can also reveal potential overfitting.
