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What is New Criterion for Interpretable Decision Trees about?

This paper proposes a new splitting criterion for decision trees that enhances interpretability by focusing on generating homogeneous rules with a significant number of instances. The method utilizes an adjustable hyperparameter to control rule coverage and employs a heuristic algorithm for optimal parameter selection. Experimental results demonstrate that trees constructed using this criterion yield highly interpretable rules while maintaining comparable predictive performance to existing methods.

Author

rohititeng

Language

EN