The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.

<p>The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.</p>

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Bibliographic Details
Main Author:	Javad Hemmatian (20700209) (author)
Other Authors:	Rassoul Hajizadeh (20700212) (author), Fakhroddin Nazari (20700215) (author)
Published:	2025
Subjects:	Medicine Science Policy Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Information Systems not elsewhere classified using five metrics oversampling minority classes neighbors &# 8217 matthew &# 8217 maternal health risk experimental findings indicate enn methods across cohen &# 8217 category form one become increasingly prominent achieving average improvements smote consistently outperformed smote combines smote smote ), smote smote outperformed rn four imbalanced datasets mcc ), f1 imbalanced data xlink "> two clusters tomek link study proposes results demonstrate recent years proposed method novel data machine learning correlation coefficient classification algorithms
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The methodology for dividing the samples of datasets for evaluation in classification methods: (a) splitting the datasets into training and test sets for datasets with sufficient samples; (b) using k-fold cross-validation for evaluating datasets with fewer samples.

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