Identifying overfitting and underfitting in an Artificial Neural Network (ANN) model involves analyzing the model’s performance on training, validation, and test datasets.
Indicators of Overfitting and Underfitting -
- High Training Accuracy, Low Validation/Test Accuracy: If the model shows significantly higher accuracy on the training set compared to the validation or test set, it is likely overfitting.
- Low Training and Validation/Test Accuracy: If the model performs poorly on both the training and validation/test sets, it is likely underfitting.
Methods to detect Overfitting and Underfitting -
- Learning Curves: Check this medium article that describes how to use "Learning Curve to identify Overfitting and Underfitting in a model".
