Grid Search Hyper-Parameter Tuning and K-Means Clustering toImprove the Decision Tree Accuracy
Shivam Kumar, Tushar Singh, Smıta Sıngh, Shivam Kumar Singh · Zenodo (CERN European Organization for Nuclear Research) · 2022
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.