Inductive learning – decision trees and random forests

Inductive learning attempts to discover rules from data. The most popular use of inductive learning is for the classification task. The ID3 algorithm recursively separates data into groups such that within each group, data are homogeneous. Shannon’s entropy function is an important concept in information theory and is widely adopted for various tasks in computer science. Decision trees are sensitive to the values of variables in individual data points. Inductive learning is the process of discovering generalized rules from data. Ensemble learning makes use of multiple machine learning models that are executed in parallel (bagging) or in sequence (boosting). Random forests use bagging to generate multiple predictions from several decision trees, and these are aggregated to compute the final prediction.