Desarrollo de una librería Haskell sobre árboles de decisión

Abstract

A Decision Tree is a supervised learning technique used for prediction tasks, which stands out for being able to produce interpretable classification and regression models. This project includes an overview of some Machine Learning, specifically Decision Trees, concepts and algorithms. The aim of this work is to obtain a clear and readable implementation of algorithms C4.5 and CART, pruning and a simple Random Forest in the functional programming language Haskell.