https://sdq.kastel.kit.edu/index.php?title=Predictability_of_Classi%EF%AC%81cation_Performance_Measures_with_Meta-Learning&feed=atom&action=historyPredictability of Classification Performance Measures with Meta-Learning - Versionsgeschichte2024-03-28T20:54:06ZVersionsgeschichte dieser Seite in SDQ-InstitutsseminarMediaWiki 1.39.6https://sdq.kastel.kit.edu/mediawiki-institutsseminar/index.php?title=Predictability_of_Classi%EF%AC%81cation_Performance_Measures_with_Meta-Learning&diff=944&oldid=prevJakob.bach@kit.edu: Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Huijie Wang |email=upssh@student.kit.edu |vortragstyp=Proposal |betreuer=Jakob Bach |termin=Institutsseminar/2019-04-12 |kurzfassung=In…“2019-04-02T08:48:13Z<p>Die Seite wurde neu angelegt: „{{Vortrag |vortragender=Huijie Wang |email=upssh@student.kit.edu |vortragstyp=Proposal |betreuer=Jakob Bach |termin=Institutsseminar/2019-04-12 |kurzfassung=In…“</p>
<p><b>Neue Seite</b></p><div>{{Vortrag<br />
|vortragender=Huijie Wang<br />
|email=upssh@student.kit.edu<br />
|vortragstyp=Proposal<br />
|betreuer=Jakob Bach<br />
|termin=Institutsseminar/2019-04-12<br />
|kurzfassung=In machine learning, classification is the problem of identifying to which of a set of categories a new instance belongs. Usually, we cannot tell how the model performs until it is trained. Meta-learning, which learns about the learning algorithms themselves, can predict the performance of a model without training it based on meta-features of datasets and performance measures of previous runs. Though there is a rich variety of meta-features and performance measures on meta-learning, existing works usually focus on which meta-features are likely to correlate with model performance using one particular measure. The effect of different types of performance measures remain unclear as it is hard to draw a comparison between results of existing works, which are based on different meta-data sets as well as meta-models. The goal of this thesis is to study if certain types of performance measures can be predicted better than other ones and how much does the choice of the meta-model matter, by constructing different meta-regression models on same meta-features and different performance measures. We will use an experimental approach to evaluate our study.<br />
}}</div>Jakob.bach@kit.edu