A Study of Supervised Learning in Context with Decision and Regression Tree

Abstract

One of the major objectives of machine learning is to instruct computers to use data or past experience to solve a specific task. Machine learning can be applied as association analysis through supervised learning, unsupervised learning but this paper will focus on strength of supervised learning classification algorithms. Section 1, describes the supervised learning with training sets. The function of Machine learning attempts is to tell system how to automatically find a good predictor based on past experiences. Section 2, deal with the supervised learning algorithm; the goal of supervised learning is to build a concise model of the distribution of class labels in terms of predictor features and analysis the decision tree and regression tree. Last section, describes the decision tree and regression tree and their results. The resulting classifier is then used to assign class labels to the testing instances where the values of the predictor features are known. The regression tree gives the value in term of continuous data.