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Thesis

Empirical performance of simple statistical inference-type models with feature extraction in comparison to modern machine learning methods concerning regression and classification problems

Abstract:

While most statistical methods used in the industry are of simple structure -- like decision trees and logistic regression -- many machine learning competitions are won by modern complex methods, which also outperformed the more simple methods in previous benchmark studies. In these benchmark studies, the results were usually obtained over raw and unprocessed datasets. In practice, when developing a model, a feature extraction is performed first to create non-redundant and useful covariate...

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Division:
MPLS
Department:
Mathematical Institute
Role:
Author

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Role:
Supervisor
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Supervisor
Type of award:
MSc
Level of award:
Masters
Awarding institution:
University of Oxford
Keywords:
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UUID:
uuid:470caa81-e106-4c87-af02-1aeaf6380269
Deposit date:
2019-10-03

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