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Supervised machine learning algorithms for predicting stude..:
Alice Villar
;
Carolina Robledo Velini de Andrade
https://doi.org/10.1007/s44163-023-00079-z. , 2024
Link:
https://doi.org/10.1007/s44163-023-00079-z
RT Journal T1
Supervised machine learning algorithms for predicting student dropout and academic success: a comparative study
UL https://suche.suub.uni-bremen.de/peid=base-ftdoajarticles:oai:doaj.org_article:77b56ba5bc4041b39ffa0259b31e6c5f&Exemplar=1&LAN=DE A1 Alice Villar A1 Carolina Robledo Velini de Andrade PB Springer YR 2024 K1 Dropout prediction K1 Supervised classification algorithms K1 Unsupervised classification algorithms K1 Class imbalance K1 Boosting algorithms K1 Hyperparameters K1 Computational linguistics. Natural language processing K1 P98-98.5 K1 Electronic computers. Computer science K1 QA75.5-76.95 JF https://doi.org/10.1007/s44163-023-00079-z LK http://dx.doi.org/https://doi.org/10.1007/s44163-023-00079-z DO https://doi.org/10.1007/s44163-023-00079-z SF ELIB - SuUB Bremen
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