Publication Date: 2022/09/29
Abstract: Representation and quality of the instance data are the foremost factors that affects classification accuracy of the statistical - based method Decision tree algorithm which gives less accuracy for binary classification problems. Experiments shows that by using clustering and hyper-parameter tuning, the decision tree accuracy can be achieved above 95%, better than the 75% recognition using decision tree alone.
Keywords: Classification, Clustering, K-means, Decision Tree, Hyper-parameter Tuning, Grid Search, Customer Churn, Logistic Regression.
DOI: https://doi.org/10.5281/zenodo.7121626
PDF: https://ijirst.demo4.arinfotech.co/assets/upload/files/IJISRT22SEP233.pdf
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