Evaluating the Impact of Data Augmentation on Predictive Model Performance

In supervised machine learning (SML) research, large training datasets are essential for valid results. However, obtaining primary data in learning analytics (LA) is challenging. Data augmentation can address this by expanding and diversifying data, though its use in LA remains underexplored. This p...

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Veröffentlicht in:	arXiv.org 2024-12
Hauptverfasser:	Švábenský, Valdemar, Borchers, Conrad, Cloude, Elizabeth B, Shimada, Atsushi
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Computers and Society Computer Science - Learning Data augmentation Machine learning Noise prediction Performance evaluation Prediction models Sampling methods Statistical methods Supervised learning
Online-Zugang:	Volltext
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