Return to search

Odhadování přesnosti klasifikačních metod na základě vlasnosti dat / Estimating performance of classifiers from dataset properties

The following thesis explores the impact of the dataset distributional prop- erties on classification performance. We use Gaussian copulas to generate 1000 artificial dataset and train classifiers on them. We train Generalized linear models, Distributed Random forest, Extremely randomized trees and Gradient boosting machines via H2O.ai machine learning platform accessed by R. Classi- fication performance on these datasets is evaluated and empirical observations on influence are presented. Secondly, we use real Australian credit dataset and predict which classifier is possibly going to work best. The predicted perfor- mance for any individual method is based on penalizing the differences between the Australian dataset and artificial datasets where the method performed com- paratively better, but it failed to predict correctly. 1

Identiferoai:union.ndltd.org:nusl.cz/oai:invenio.nusl.cz:388668
Date January 2018
CreatorsTodt, Michal
ContributorsPolák, Petr, Baruník, Jozef
Source SetsCzech ETDs
LanguageEnglish
Detected LanguageEnglish
Typeinfo:eu-repo/semantics/masterThesis
Rightsinfo:eu-repo/semantics/restrictedAccess

Page generated in 0.0016 seconds