Understanding model fitting is important for understanding the models’ poor accuracy.
Overfitting: When the model performs too well on training data then it reduces the model flexibility for new data.
Underfitting: When the model performs poorly on the training data. It’s often caused by an excessively simple model.
Both overfitting and underfitting lead to poor performance in real time.
Balanced: Bbalanced models would show better accuracy on new data.