Iterative Dichotomizer 3 (ID3) Decision Tree: A Machine Learning Algorithm for Data Classification and Predictive Analysis

Edward E. Ogheneovo, Promise A. Nlerum

Decision trees, ID3, Decision trees, machine learning, entropy, information gain.

Decision trees are very important machine learning algorithms used for the classification and predictive analytic purposes in computer science and related disciplines. ID3 decision tree algorithm was designed by Quinlan in 1986.The algorithm is based on Hunt’s algorithm and was serially implemented. ID3 tree is constructed in two phases: tree building and tree pruning. Data is sorted at every node during the tree building phase to choose the best splitting single attribute. The main ideas behind the ID3 algorithm are: 1) each non-leaf node of a decision tree corresponds to an input attribute, and each arc to a possible value of that attribute. In this paper, ID3, a machine learning algorithm is used to predict weather condition for an out tennis match. The paper demonstrates the use of ID3 decision tree to predict weather conditions with outlooks such as sunny, overcast, and rain; temperature conditions such as hot, mild, and cool; humidity conditions such as high and normal; wind conditions such as weak and strong and the necessary conditions such as yes or no. Based on the results computed using the entropy and information gains, a decision tree is constructed thus providing information for tennis and other sports athletes who wish to play out-door games.

10.22161/ijaers.74.60