The rules extraction from the Decision Tree can help with better understanding how samples propagate through the tree during the prediction. It can be needed if we want to implement a Decision Tree without Scikit-learn or different than Python language. Decision Trees are easy to move to any programming language because there are set of if-else
statements. I’ve seen many examples of moving scikit-learn Decision Trees into C, C++, Java, or even SQL.
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Extract Rules from Decision Tree in 3 Ways with Scikit-Learn and Python
February 25, 2021 by Piotr Płoński Decision tree Scikit learn
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Visualize a Decision Tree in 4 Ways with Scikit-Learn and Python
June 22, 2020 by Piotr Płoński Decision tree
A Decision Tree is a supervised algorithm used in machine learning. It is using a binary tree graph (each node has two children) to assign for each data sample a target value. The target values are presented in the tree leaves. To reach to the leaf, the sample is propagated through nodes, starting at the root node. In each node a decision is made, to which descendant node it should go. A decision is made based on the selected sample’s feature. Decision Tree learning is a process of finding the optimal rules in each internal tree node according to the selected metric.