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Rule-based models in machine learning utilize a set of interpretable rules for decision-making, with decision trees being a prominent example that structures decisions in a tree-like format. These models, including rule-based classifiers and association rule mining, excel in explainability but may struggle with complex relationships and noise in data. Techniques such as pruning and attribute selection measures like Information Gain and Gini Index are essential for optimizing decision trees and enhancing the

Author

vishnupadhala2000

Language

EN