Holdout evaluation of performance on test set using neural network, random forest and support vector machine (SVM) models.

<p>Holdout evaluation of performance on test set using neural network, random forest and support vector machine (SVM) models.</p>

Publication Details

Journal
Figshare
Published
2024-10-25
DOI
https://doi.org/10.1371/journal.pdig.0000636.s004
Primary Topic
Engineering and Test Systems
Type
article
Field-Weighted Citation Impact
0.00
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article

Holdout evaluation of performance on test set using neural network, random forest and support vector machine (SVM) models.

Figshare
Engineering and Test Systems
article

Holdout evaluation of performance on test set using neural network, random forest and support vector machine (SVM) models.

article en

Abstract

<p>Holdout evaluation of performance on test set using neural network, random forest and support vector machine (SVM) models.</p>

Figshare
Openalex Percentile: Top 57%
Engineering and Test Systems
DOISourceOpenAlex
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Holdout evaluation of performance on test set using neural network, random forest and support vector machine (SVM) models. · Figshare (2024) | TGRS Research Map | TGRS