The other extreme of the training phase is termed as overfitting. The overfitting graph can be represented as follows:

This shows a target function that perfectly maps each data point in our training dataset. This is better known as model overfitting. In such cases, the algorithm tries to learn the exact data characteristics, including the noise, and thus fails to predict reliably on new unseen data points.