Impacts of Evaluation Methods on Classification Algorithm s Accuracy

Abstract

Decision trees are one of the most powerful and commonly used supervised learning algorithms in the field of data mining. It is important that a decision tree perform accurately when employed on unseen data; therefore, evaluation methods are used to measure the predictive performance of a decision tree classifier. However, the predictive accuracy of a decision tree is also dependent on the evaluation method chosen since training and testing sets of decision tree models are selected according to the evaluation methods. The aim of this paper was to study and understand how using different evaluation methods might have an impact on decision tree accuracies when they are applied to different decision tree algorithms.