Exploring the novel support points-based split method on a soil dataset

Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F60460709%3A41210%2F21%3A85643" target="_blank" >RIV/60460709:41210/21:85643 - isvavai.cz</a>
Výsledek na webu
<a href="https://doi.org/10.1016/j.measurement.2021.110131" target="_blank" >https://doi.org/10.1016/j.measurement.2021.110131</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.measurement.2021.110131" target="_blank" >10.1016/j.measurement.2021.110131</a>

Jazyk výsledku
angličtina
Název v původním jazyce
Exploring the novel support points-based split method on a soil dataset
Popis výsledku v původním jazyce
Data splitting is an integral step in machine learning that ensures good model generalization. The novel support points based split method has been evaluated on several datasets and has shown to be promising than conventional methods . However, this method has never been applied in soil based research. Therefore, the current study compared soil organic carbon RMSE prediction results generated through the conventional random split and the novel support points-based split methods.
Název v anglickém jazyce
Exploring the novel support points-based split method on a soil dataset
Popis výsledku anglicky
Data splitting is an integral step in machine learning that ensures good model generalization. The novel support points based split method has been evaluated on several datasets and has shown to be promising than conventional methods . However, this method has never been applied in soil based research. Therefore, the current study compared soil organic carbon RMSE prediction results generated through the conventional random split and the novel support points-based split methods.

Rok uplatnění
2021
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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