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Noise Resilience in ML: Trees vs. Regression by Shiv Patel is a document available to read on EtoBox.

This study compares the noise resilience of decision trees and linear regression in machine learning, hypothesizing that decision trees are more robust to noise in input data. Experiments using the IPARC dataset showed that decision trees maintained higher accuracy and lower error rates compared to linear regression when noise was introduced. The findings suggest that decision trees are better suited for applications involving noisy data, with potential for future research into other models and preprocessin

Author

Shiv Patel

Language

EN