Tree-Based Modeling Techniques

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Author(s): Dileep Kumar G. (Adama Science and Technology University, Ethiopia)
Copyright: 2019
Pages: 18
Source title: Machine Learning Techniques for Improved Business Analytics
Source Author(s)/Editor(s): Dileep Kumar G. (Adama Science and Technology University, Ethiopia)
DOI: 10.4018/978-1-5225-3534-8.ch001

Keywords: Business Information Systems / Business Science Reference / Data Analysis & Statistics / Library & Information Science

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Abstract

Tree-based learning techniques are considered to be one of the best and most used supervised learning methods. Tree-based methods empower predictive models with high accuracy, stability, and ease of interpretation. Unlike linear models, they map non-linear relationships pretty well. These methods are adaptable at solving any kind of problem at hand (classification or regression). Methods like decision trees, random forest, gradient boosting are being widely used in all kinds of machine learning and data science problems. Hence, for every data analyst, it is important to learn these algorithms and use them for modeling. This chapter guide the learner to learn tree-based modeling techniques from scratch.

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