Is your model overfitting? Or maybe underfitting? An example using a neural network in python

Underfitting means that our ML model can neither model the training data nor generalize to new unseen data. A model that underfits the data will have poor performance on the training data. For example, in a scenario where someone would use a linear model to capture non-linear trends in the data, the model would underfit the data. A textbook case of underfitting is when the model's error on both the training and test sets (i.e. during training and testing) is very high. It is obvious that there is a trade-off between overfitting and underfitting.