LAUSR

Data splitting to avoid information leakage with DataSAIL

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0 Published in 2025 at "Nature Communications"

DOI: 10.1038/s41467-025-58606-8

Abstract: Information leakage is an increasingly important topic in machine learning research for biomedical applications. When information leakage happens during a model’s training, it risks memorizing the training data instead of learning generalizable properties. This can… read more here.

Keywords: information leakage; leakage; data splitting; machine learning ... See more keywords

Two cross-validation techniques to comprehensively characterize global horizontal irradiation regression models: Single data-splitting is insufficient

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0 Published in 2019 at "Journal of Renewable and Sustainable Energy"

DOI: 10.1063/1.5116642

Abstract: Data-splitting is the most widely used method to cross-validate global horizontal irradiation regression models. An available dataset is split into two subsets, one to calibrate models and the other to validate them. This study investigated… read more here.

Keywords: validation; cross validation; data splitting; regression models ... See more keywords

Improving data splitting for classification applications in spectrochemical analyses employing a random-mutation Kennard-Stone algorithm approach

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0 Published in 2019 at "Bioinformatics"

DOI: 10.1093/bioinformatics/btz421

Abstract: Abstract Motivation Data splitting is a fundamental step for building classification models with spectral data, especially in biomedical applications. This approach is performed following pre-processing and prior to model construction, and consists of dividing the… read more here.

Keywords: classification; algorithm; splitting classification; random mutation ... See more keywords