XGBoost: How Deep Learning Can Replace Gradient Boosting and Decision Trees — Part 2: Training | by Saupin Guillaume | Sep, 2023

In a earlier article:

you’ve discovered about rewriting choice bushes utilizing a Differentiable Programming method, as advised by the NODE paper. The thought of this paper is to switch XGBoost by a Neural Community.

Extra particularly, after explaining why the method of constructing Determination Bushes just isn’t differentiable, it launched the required mathematical instruments to regularize the 2 predominant components related to a call node:

• Characteristic Choice
• Department detection

The NODE paper reveals that each might be dealt with utilizing the entmax operate.

To summarize, now we have proven create a binary tree with out utilizing comparability operators.

The earlier article ended with open questions concerning coaching a regularized choice tree. It’s time to reply these questions.

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First, primarily based on what we introduced within the earlier article, let’s create a brand new Python class: SmoothBinaryNode .

This class encodes the habits of a clean binary node. There are two key components in its code :

• The number of the options, dealt with by the operate _choices
• The analysis of those options, with respect to a given threshold, and the identification of the trail to comply with: left or proper . All that is managed by the strategies left and proper .