Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/331963
Type: DISSERTAÇÃO DIGITAL
Degree Level: Mestrado
Title: Comparação empírica de 16 algoritmos de regressão em 59 datasets
Title Alternative: Empirical comparison of 16 regression algorithms on 59 datasets
Author: Frondana, Giovani, 1981-
Advisor: Wainer, Jacques, 1958-
Abstract: Resumo: Foram testados 16 algoritmos de regressão (random forest, support vector machine linear, polinomial e radial, 1-hidden-layer neural network, gradient boosting machine, k-nearest neighbor, generalized linear model com regularização lasso ou elasticnet, multivariate adaptive regression splines, cubist, relevance vector machine, partial least squares, principal component regression, extreme learning machine, RBF network e gaussian process) em 59 datasets reais, com as métricas MAE e MSE. Os algoritmos foram comparados segundo os testes de Friedman com post-hoc Nemenyi e Wilcoxon corrigido por Hommel e por meio de análise bayesiana. Os resultados sugerem que o melhor algoritmo de regressão é o cubist, ainda que para fins práticos, em datasets muito grandes, a melhor opção seja o gradient boosting machine

Abstract: We evaluated 16 regression algorithms (random forest, linear, polynomial and radial support vector machines, 1-hidden-layer neural network, gradient boosting machine, k-nearest neighbor, generalized linear model with regularization lasso or elasticnet, multivariate adaptive regression splines, cubist, relevance vector machine, partial least squares, principal component regression, extreme learning machine, RBF network e gaussian process) on 59 real datasets with MAE and MSE metrics. For comparisons, we followed Friedman test with Nemenyi post-hoc, Wilcoxon corrected by Hommel procedure and Bayesian analysis. The results suggest that the best regression algorithm is cubist, although for practical purposes, in very large datasets, the best option is gradient boosting machine
Subject: Comparações múltiplas (Estatística)
Análise de regressão
Análise de algoritmos
Aprendizado de máquina
Editor: [s.n.]
Date Issue: 2017
Appears in Collections:IC - Tese e Dissertação

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