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Photo by Simon Wilkes on Unsplash In a previous article: you have learned about rewriting decision trees using a Differentiable Programming approach, as suggested by the NODE paper. The idea of this paper is to replace XGBoost by a Neural Network. More specifically, after explaining why the process of building Decision Trees is not differentiable, it introduced the necessary mathematical tools to regularize the two main elements associated with a decision node: Feature Selection Branch detection The NODE paper shows that both can be handled using the entmax function. To summarize, we have shown how to create a binary tree without using comparison operators. The previous article ended with open questions regarding training a regularized decision tree. It's time to answer these questions. If you're interested in a deep dive in Gradient Boosting Methods, have a look at my book: First, based on what we presented in the previous article, let's create a new Python class: Smoot

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