Počet záznamů: 1
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics
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SYSNO ASEP 0563344 Druh ASEP J - Článek v odborném periodiku Zařazení RIV J - Článek v odborném periodiku Poddruh J Článek ve WOS Název Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics Tvůrce(i) Marmolejo-Ramos, F. (AU)
Tejo, M. (CL)
Brabec, Marek (UIVT-O) RID, SAI, ORCID
Kužílek, J. (CZ)
Joksimovic, S. (AU)
Kovanovic, V. (AU)
González, J. (CL)
Kneib, T. (DE)
Bühlmann, P. (CH)
Kook, L. (CH)
Briseño-Sánchez, G. (DE)
Ospina, R. (BR)Celkový počet autorů 12 Číslo článku e1479 Zdroj.dok. Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery - ISSN 1942-4787
Roč. 13, č. 1 (2023)Poč.str. 22 s. Jazyk dok. eng - angličtina Země vyd. US - Spojené státy americké Klíč. slova causal regularization ; causality ; educational data mining ; generalized additive models for location, scale and shape ; learning analytics ; machine learning ; statistical learning ; statistical modeling ; supervised learning Obor OECD Statistics and probability Způsob publikování Open access Institucionální podpora UIVT-O - RVO:67985807 UT WOS 000870901000001 EID SCOPUS 85140231241 DOI 10.1002/widm.1479 Anotace The advent of technological developments is allowing to gather large amounts of data in several research fields. Learning analytics (LA)/educational data mining has access to big observational unstructured data captured from educational settings and relies mostly on unsupervised machine learning (ML) algorithms to make sense of such type of data. Generalized additive models for location, scale, and shape (GAMLSS) are a supervised statistical learning framework that allows modeling all the parameters of the distribution of the response variable with respect to the explanatory variables. This article overviews the power and flexibility of GAMLSS in relation to some ML techniques. Also, GAMLSS' capability to be tailored toward causality via causal regularization is briefly commented. This overview is illustrated via a data set from the field of LA. Pracoviště Ústav informatiky Kontakt Tereza Šírová, sirova@cs.cas.cz, Tel.: 266 053 800 Rok sběru 2024 Elektronická adresa https://dx.doi.org/10.1002/widm.1479
Počet záznamů: 1