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Author: Admin | 2025-04-28
Customer, given their product choices? (Regression decision tree)Which article should I recommend to my blog readers next? (Classification decision trees)Intuitive AdvantagesThere are multiple reasons why decision trees are one of the go-to machine learning algorithms in real-life applications:IntuitiveInformationalScalingMachine Learning Approach to Decision TreesDecision trees belong to a class of supervised machine learning algorithms, which are used in both classification (predicts discrete outcome) and regression (predicts continuous numeric outcomes) predictive modeling.The goal of the algorithm is to predict a target variable from a set of input variables and their attributes. The approach builds a tree structure through a series of binary splits (yes/no) from the root node via branches passing several decision nodes (internal nodes), until we come to leaf nodes.It is here that the prediction is made.Each split partitions the input variables into feature regions, which are used for lower splits. We can visualize the entire tree structure like this:Image Source: Google ImagesDecision Tree AlgorithmsThere is no single decision tree algorithm. Instead, multiple algorithms have been proposed to build decision trees:ID3: Iterative Dichotomiser 3C4.5: the successor of ID3CART: Classification And Regression TreeCHAID: Chi-square automatic interaction detectionMARS: multivariate adaptive regression splinesEach new algorithm improves upon the previous ones, with the aim of developing approaches which achieve higher accuracy with noisier or messier data.ImplementationIn general, we can break down the decision tree algorithm into a series of steps common across different implementations:Attribute selection:Start with the entire dataset and look at every feature or attribute.Look at all of the possible values of that
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