Ocenjevanje standardne napake in intervalov zaupanja z metodo bootstrap
GAŠPARAC, GRETA (Author), Štrumbelj, Erik (Mentor) More about this mentor...

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Abstract
V diplomskem delu predstavimo metodo bootstrap, ki jo uvrščamo v družino metod samovzorčenja ter je preprostejša in bolj intuitivna alternativa tradicionalnim statističnim metodam za ocenjevanje negotovosti. Osredotočimo se na neparametrično različico metode. Opišemo njene lastnosti in jih predstavimo s praktičnimi primeri s področja strojnega učenja - ocenjevanje in primerjava različnih modelov. Izpostavimo tudi šibke točke metode. Predstavimo in primerjamo tri intervale zaupanja bootstrap: standardnega normalnega z uporabo standardne napake bootstrap in dva klasična intervala bootstrap, centilnega in BCa. Pričakovano se v večini primerov najbolje obnese BCa.

Language: Slovenian metoda bootstrap, standardna napaka, intervali zaupanja Bachelor thesis/paper (mb11) FRI - Faculty of computer and information science 2018 443 260 (0 votes) Voting is allowed only to logged in users. AddThis uses cookies that require your consent. Edit consent...

Secondary language

Language: English Boostraping standard errors and confidence intervals We introduce the reader to the bootstrap, a simple and flexible resampling-based alternative for quantifying uncertainty. We describe the basic characteristics of the non-parametric bootstrap and illustrate its practical behaviour with simulations in the context of a typical task in machine learning - estimating and comparing the performance of different prediction models. We also present some of the method's weaknesses. We introduce and compare three standard intervals: the standard normal using bootstrap standard error and two more typical bootstrap confidence intervals, the percentile and the BCa interval. As theory suggests, the BCa performs the best over a wide range of situations. bootstrap, standard error, confidence intervals