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.