Feature selection with Random Forest and Gradient Boosting
Author
Alonso Liso, ÁlvaroAdvisor
Dorronsoro Ibero, José RamónEntity
UAM. Departamento de Ingeniería InformáticaDate
2016-09Subjects
InformáticaNote
Máster Universitario en Investigación e Innovación en Tecnologías de la Información y las ComunicacionesEsta obra está bajo una licencia de Creative Commons Reconocimiento-NoComercial-SinObraDerivada 4.0 Internacional.
Abstract
The objective of the present work is to analyze the problem which arose naturally working
with datasets with a large number of features, which usually forces the data analyst to select
a small subset of all the available features to obtain acceptable training times and reduce
over tting. The present work studies the usefulness of the feature importance coe cients
given by Trees, Random Forest and Gradient Boosting regressors applied to a problem of
wind energy production.
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Google Scholar:Alonso Liso, Álvaro
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Except where otherwise noted, this item's license is described as https://creativecommons.org/licenses/by-nc-nd/4.0/
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