Performance Comparison Analysis of Classifiers on Binary Classification Dataset
Abstract
In this study, we compared the performance of Random Forest Classifier and Decision Tree using classification evaluation methods. The results showed that Random Forest Classifier had a higher overall accuracy rate but also produced more outliers. On the other hand, Decision Tree demonstrated consistency in classification with fewer outliers. These findings provide insights into the trade-off between accuracy and consistency when selecting the appropriate classification method. Furthermore, further research is needed to understand the impact of outliers on classification performance and to take appropriate steps in addressing them.
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