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http://hdl.handle.net/10609/117686
Title: Optimal decision trees using optimization techniques
Author: Alòs Pascual, Josep
Director: Casas Roma, Jordi
Tutor: Parada Medina, Raúl
Keywords: decision trees
business intelligence
optimization
Issue Date: 16-Jun-2020
Publisher: Universitat Oberta de Catalunya (UOC)
Abstract: The rising need of having a way to understand and explain the decisions produced by the artificial intelligence algorithms, used in a broad set of fields, led to the apparition of the concept of explainable artificial intelligence. One of the most simple, although powerful, algorithms are the decision trees. This project focuses on studying the algorithms that allow the creation of such trees, while ensuring that the tree is optimal, as smaller trees are usually easier to explain. The project presents a Python package whose purpose is to act as a barrier remover for the users that don't have the means to implement those algorithms, allowing them to use the implementations proposed by different authors while leveraging the implementation of both the algorithms and the interaction with the solving ls to the package. In this report, the design of such tool is presented, as well as the technical considerations on which solving tools are used. Also, benchmarking on different datasets used in the bibliography is done to assess that the package accomplishes its main task, and to compare the different approaches implemented.
Language: English
URI: http://hdl.handle.net/10609/117686
Appears in Collections:Bachelor thesis, research projects, etc.

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