An effective approach for determining sample size that optimizes the performance of classifier

determining sample size that optimizes the performance of classifier

Authors

Keywords:

: Learning curve, model optimization, random forest, effective sample size

Abstract

The goal of machine learning is to create a model that performs well and gives accurate prediction outcome in a particular set of
classification tasks. In order to achieve higher performance, machine-learning model has to be optimized. Literature shows that parameter
and hyper-parameter tuning is most widely used for model optimization. In most classification tasks, the dataset is divided into training
and testing set with 70% and 30% for training and test respectively. However, the 70% training and 30% testing set division does not
grantee better predictive outcome for all classification. Thus, this study proposes learning curve for analysis of the effect of data size on
the performance of classification model using real world heart disease dataset employing random forest model. The experimental result
shows that data sample size has significant effect on the performance of random forest model. Learning curve is the best approach for
determining the sample size for classification task using machine-learning model.

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References

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https://doi.org/10.1186/s12859-020-03626-y
Performance of random forest on heart disease dataset.

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Published

27.05.2022

How to Cite

Assegie, T. A. (2022). An effective approach for determining sample size that optimizes the performance of classifier: determining sample size that optimizes the performance of classifier. International Journal of Intelligent Systems and Applications in Engineering, 10(2), 222–225. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/1485

Issue

Vol. 10 No. 2 (2022)

Section

Research Article

License

Copyright (c) 2022 Tsehay Admassu Assegie

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