Nicely done, only somewhat technical ... worth understanding, intro below, more at link. Its often very useful to construct a decision tree to understand a process being modelled before going deeper.
Towards Data Science by Shailey Dash / November 02, 2022
Decision Trees Explained — Entropy, Information Gain, Gini Index, CCP Pruning
Though Decision Trees look simple and intuitive, there is nothing very simple about how the algorithm goes about the process deciding on splits and how tree pruning occurs. In this post I take you through a simple example to understand the inner workings of Decision Trees.
Decision Trees are a popular and surprisingly effective technique, particularly for classification problems. But, the seemingly intuitive interface hides complexities. The criterion for selecting variables and hierarchy can be tricky to get, not to mention Gini index, Entropy ( wait, isn’t that physics?) and information gain (isn’t that information theory?). As you can see there are lots of tricky problems on which you can get stuck on. The best way to understand Decision Trees is to work through a small example which has sufficient complexity to be able to demonstrate some of the common points one suddenly goes, ‘ not sure what happens here…?’.
This post is therefore more like a tutorial or a demo where I will work through a toy dataset that I have created to understand the following: ... .'
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