Given a set of classified examples a decision tree is induced, biased by the information gain measure, which heuristically leads to small trees. Therefore I want to know what am I doing wrong in calculating Information Gain or where does the problem lies? The algorithm follows a greedy approach by selecting a best attribute that yields maximum information gain (IG) or minimum entropy (H). The set of possible classes is finite. The algorithm ID3 (Quinlan) uses the method top-down induction of decision trees. This article targets to clearly explain the ID3 Algorithm (one of the many Algorithms used to build Decision Trees) in detail. The examples are given in attribute-value representation. Algorithms used in Decision Tree. To run this example with the source code version of SPMF, launch the file "MainTestID3.java" in the package ca.pfv.SPMF.tests. ID3 To understand this concept, we take an example, assuming we have a data set (link is given here Click Here). Algorithm Concepts. Before we introduce the ID3 algorithm lets quickly come back to the stopping criteria of the above grown tree. We explain the algorithm using a fake sample Covid-19 dataset. This post will give an overview on how the algorithm works. Algorithms. In simple… ID3 is a classification algorithm which for a given set of attributes and class labels, generates the model/decision tree that categorizes a given input to a specific class label Ck [C1, C2, …, Ck]. Based on this data, we have to find out if we can play someday or not. This example explains how to run the ID3 algorithm using the SPMF open-source data mining library.. How to run this example? We can define a nearly arbitrarily large number of stopping criteria. Besides the ID3 algorithm there are also other popular algorithms like the C4.5, the C5.0 and the CART algorithm which we will not further consider here. For example can I play ball when the outlook is sunny, the temperature hot, the humidity high and the wind weak. In this small sample of the dataset with the ID3 parameter it gives me a tree but when I run the same code with all of the datapoints from the dataset I get just a 0 value. Introduction. CART (Gini Index) ID3 (Entropy, Information Gain) Note:-Here we will understand the ID3 algorithm . For more detailed information please see the later named source. Different libraries of different programming languages use particular default algorithms to build a decision tree but it is quite unclear for a data scientist to understand the difference between the algorithms used. I will focus on the C# implementation. A decision tree is a classification algorithm used to predict the outcome of an event with given attributes. SPMF documentation > Creating a decision tree with the ID3 algorithm to predict the value of a target attribute . Here we will discuss those algorithms.