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@inproceedings{1414250,
author = {Pelánek, Radek},
address = {USA},
booktitle = {Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization},
doi = {http://dx.doi.org/10.1145/3099023.3099042},
keywords = {AUC; evaluation; metrics; predictive accuracy; RMSE; student modeling},
howpublished = {elektronická verze "online"},
language = {eng},
location = {USA},
isbn = {978-1-4503-5067-9},
pages = {197-201},
publisher = {ACM},
title = {Measuring predictive performance of user models: The details matter},
year = {2017}
}
TY - JOUR
ID - 1414250
AU - Pelánek, Radek
PY - 2017
TI - Measuring predictive performance of user models: The details matter
PB - ACM
CY - USA
SN - 9781450350679
KW - AUC
KW - evaluation
KW - metrics
KW - predictive accuracy
KW - RMSE
KW - student modeling
N2 - Evaluation of user modeling techniques is often based on the predictive accuracy of models. The quantification of predictive accuracy is done using performance metrics. We show that the choice of a performance metric is important and that even details of metric computation matter. We analyze in detail two commonly used metrics (AUC, RMSE) in the context of student modeling. We discuss different approaches to their computation (global, averaging across skill, averaging across students) and show that these methods have different properties. An analysis of recent research papers shows that the reported descriptions of metric computation are often insufficient. To make research conclusions valid and reproducible, researchers need to pay more attention to the choice of performance metrics and they need to describe more explicitly details of their computation
ER -
PELÁNEK, Radek. Measuring predictive performance of user models: The details matter. Online. In \textit{Adjunct Publication of the 25th Conference on User Modeling, Adaptation and Personalization}. USA: ACM, 2017, s.~197-201. ISBN~978-1-4503-5067-9. Dostupné z: https://dx.doi.org/10.1145/3099023.3099042.
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