Assessing the Usability of Predictions of Different Regression Models

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F10%3A00348393" target="_blank" >RIV/67985807:_____/10:00348393 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Assessing the Usability of Predictions of Different Regression Models
Original language description
Two kinds of methods for assessing reliability of regression models are analysed. The first kind consists in obtaining confidence intervals using either statistical methods (frequentist, Bayesian) or transductive inference. The second kind are heuristicmethods, designed so that they correlate with prediction error. Both methods are compared on four kinds of parametric regression models using real-world data. This is a work in progress, that is why transductive inference has not been implemented yet, only one dataset has been used, and the comparison has been implemented for two kinds of regression models, so far: multilayer perceptron and support-vector regression. For them, the variability of results obtained by heuristics methods with respect to different models is illustrated, as well as the correlation between the confidence intervals and the heuristic methods.
Czech name
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Czech description
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Project
<a href="/en/project/GA201%2F08%2F0802" target="_blank" >GA201/08/0802: Applications of Methods of Knowledge Engineering in Data Mining</a><br>
Continuities
Z - Vyzkumny zamer (s odkazem do CEZ)

Publication year
2010
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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