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Genetic Programming as Supervised Machine Learning Algorithm
Abstract
This chapter presents the theory and procedures behind supervised machine learning and how genetic programming can be applied to be an effective machine learning algorithm. Due to simple and powerful concept of computer programs, genetic programming can solve many supervised machine learning problems, especially regression and classifications. The chapter starts with theory of supervised machine learning by describing the three main groups of modelling: regression, binary, and multiclass classification. Through those kinds of modelling, the most important performance parameters and skill scores are introduced. The chapter also describes procedures of the model evaluation and construction of confusion matrix for binary and multiclass classification. The second part describes in detail how to use genetic programming in order to build high performance GP models for regression and classifications. It also describes the procedure of generating computer programs for binary and multiclass calcification problems by introducing the concept of predefined root node.
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