> But not so fast. B, for instance, might be correlated with A. Exactly. Rendering a model explainable is an active field of research at the moment. One way to

> But not so fast. B, for instance, might be correlated with A. Exactly. Rendering a model explainable is an active field of research at the moment. One way to tackle this is to use Shapley values from game theory. Here is a compilation of techniques to render a model explainable: https://christophm.github.io/interpretable-ml-book/shapley.html https://christophm.github.io/interpretable-ml-book/shapley.h..., and they have an elegant way to talk about these values: > The Shapley value is the average marginal contribution of a feature value over all possible coalitions. Now, it doesn't identify correlated variables (which you can do with other techniques), but will balance the influence in a robust manner. ---- > The story only gets more complicated for e.g. Random Forests. Funny you should say this. Recently there was a challenge for explainable machine learning: http://explainable.ml http://explainable.ml My proposal to the challenge was based on Random Forests. Here is the code: https://github.com/benoitparis/explainable-challenge https://github.com/benoitparis/explainable-challenge, here is the paper: https://github.com/benoitparis/explainable-challenge/raw/master/Decision%20tree%20node%20participation%20as%20a%20tool%20for%20interpretability.pdf https://github.com/benoitparis/explainable-challenge/raw/mas... I have a neat visualization for exploratory data analysis (available in the paper and in a notebook) that I'm proud of. ---- By the way if anyone is hiring here, I'm available for talking about your problem and see if my take on explainable machine learning can help you.