Grid search hyperparameter tuning and kmeans clustering toimprove the decision tree accuracy| International Journal of Innovative Science and Research Technology

Grid Search Hyper-Parameter Tuning and K-Means Clustering toImprove the Decision Tree Accuracy

Authors : Shivam Kumar; Tushar Singh; Smita Singh; Shivam Singh

Volume/Issue : Volume 7 - 2022, Issue 9 - September

Google Scholar : https://bit.ly/3IIfn9N

DOI : https://doi.org/10.5281/zenodo.7121626

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.

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.

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