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National Repository of Grey Literature 2 records found  Search took 0.01 seconds. 
Estimating performance of classifiers from dataset properties
 Todt, Michal ; Polák, Petr (advisor) ; Baruník, Jozef (referee)
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
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Variation of Relationship between Individual and Parental Education across OECD Countries
 Todt, Michal ; Chytilová, Julie (advisor) ; Želinský, Tomáš (referee)
This thesis investigates the presence of intergenerational transitions of education and how it relates to wealth. The analysis is conducted on a set of 30 OECD countries. Linear regression is used to show the presence of positive, signiffcant effects of maternal and paternal education on individual's education. Additionally, the we evaluate the functional form of the relationship between marginal effects of parental education and wealth. The datasets do not provide any supportive evidence for the hypothesis increasing of marginal effects being increasing and concave function of wealth on the interval of feasible wealth values. Moreover, the obtained positive marginal effects are likely to suffer by endogeneity bias.
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