Interpretable Neural Networks With PyTorch

There are several approaches to rate machine learning models, two of them being accuracy and interpretability. A model with high accuracy is what we usually call a good model, it learned the relationship between the inputs X and outputs y well. If a model has high interpretability or explainability, we understand how the model makes a prediction and how we can influence this prediction by changing input features. While it is hard to say how the output of a deep neural network behaves when we increase or decrease a certain feature of the input, for a linear model it is extremely easy: if you increase the feature by one, the output increases by the coefficient of that feature. "There are interpretable models an there are well-performing models."