Abstract
Data is the most valuable resource in the present. Classifying the data and using the classified data to make a decision holds the highest priority. Computers are trained to manage the data automatically using machine learning algorithms and making judgments as outputs. Several data mining algorithms can be obtained for Artificial Neural Network classification, Nearest Neighbor Law and Baysen classifiers, but the decision tree mining is most commonly used. Data can be classified easily using the decision tree classification learning process. It’s trained on a training dataset and then implemented on a test set from which a result is expected. There are three decision trees (ID3 C4.5 and CART) that are extensively used. The algorithms are all based on Hut’s algorithm. This paper focuses on the difference between the working processes, significance, and accuracy of the three (ID3 C4.5 and CART) algorithms. Comparative analysis among the algorithms is illustrated as well.
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Javed Mehedi Shamrat, F.M., Ranjan, R., Hasib, K.M., Yadav, A., Siddique, A.H. (2022). Performance Evaluation Among ID3, C4.5, and CART Decision Tree Algorithm. In: Ranganathan, G., Bestak, R., Palanisamy, R., Rocha, Á. (eds) Pervasive Computing and Social Networking. Lecture Notes in Networks and Systems, vol 317. Springer, Singapore. https://doi.org/10.1007/978-981-16-5640-8_11
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