Comparación empírica de dos modelos de aprendizaje automático generados mediante procesos diferentes

Alveiro Rosado Gomez; Liliana Calderón Benavides; Jorge Andrick Parra

doi:10.24054/rcta.v1i39.1369

Authors

Alveiro Rosado Gomez Universidad Francisco de Paula Santander Ocaña https://orcid.org/0000-0003-2932-3383
Liliana Calderón Benavides Universidad Autónoma de Bucaramanga https://orcid.org/0000-0001-8658-9036
Jorge Andrick Parra Universidad Autónoma de Bucaramanga https://orcid.org/0000-0002-2060-6419

DOI:

https://doi.org/10.24054/rcta.v1i39.1369

Keywords:

Machine learning, automated machine learning, empirical study, artificial intelligence

Abstract

Machine learning has been showing potential in the construction of models that represent the behavior that exists in the data, these models are used in different areas of knowledge to optimize the decisions that are made, automated machine learning is a field of work created to satisfy the demand for tools that allow the construction of models in a precise, agile and fast way and that are available for people who are not fluent in statistics and technology. This research makes the empirical comparison of the behavior of two models generated using normal and automated machine learning tools to classify demobilized people who may leave the reintegration process. There is agreement in the algorithm that was used, also in several of the attributes that were used and the evaluation suggests that, for the data set used, automated machine learning has better performance than traditional.

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