An effective approach for determining sample size that optimizes the performance of classifier
determining sample size that optimizes the performance of classifier
Keywords:: Learning curve, model optimization, random forest, effective sample size
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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Copyright (c) 2022 Tsehay Admassu Assegie
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