Discovering newer interpretable (glass-box) models
In the last decade, there have been significant efforts in both industry and in academia to create new models that can have enough complexity to find the sweet spot between underfitting and overfitting, known as the bias-variance trade-off, but retain an adequate level of explainability.
Many models fit this description, but most of them are meant for specific use cases, haven’t been properly tested yet, or have released a library or open-sourced code. However, two general-purpose ones are already gaining traction, which we will look at now.
Explainable Boosting Machine (EBM)
EBM is part of Microsoft’s InterpretML framework, which includes many of the model-agnostic methods we will use later in the book.
EBM leverages the GAMs we mentioned earlier, which are like linear models but look like this:
Individual functions f1 through fp are fitted to each feature using spline functions. Then a link...
