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The Joy of Decision Trees

Tom Jordan explains how a simple set of “if” statements forms the basis of some powerful data science algorithms:

While it is true there are techniques in machine learning that required advanced maths knowledge, some of the most widely used approaches make use of knowledge given to every child at secondary school. The line of best fit, drawn by many a student in Year 8 Chemistry, can also be known by its alter-ego, linear regression, and see applications all over machine learning. Neural networks, central to some of the most cutting-edge applications, are formed of simple mathematical models consisting of some addition and multiplication.

A personal favourite technique, and the subject of this blog, is the humble decision tree, taught in schools all over the country. This blog will take a high-level look at the theory around decision trees, an extension using random forests, and the real-world applications of these techniques.

Read on for more.

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