• Extract Rules from Decision Tree in 3 Ways with Scikit-Learn and Python

    February 25, 2021 by Piotr Płoński Decision tree Scikit learn

    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.

  • Visualize a Decision Tree in 4 Ways with Scikit-Learn and Python

    June 22, 2020 by Piotr Płoński Decision tree

    Visualize Decision Tree in Python bannerA 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.