Machine learning
Decision Tree
A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.
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Sources
- Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI: 10.1201/9781315139470 ↗
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Referenced by
Active learning Decision treeAdaBoostBaggingBayesian Decision TreeBoostingCase-Based ReasoningCatBoostEnsemble Decision TreeEnsemble Gradient BoostingExplainable Decision TreeExplainable Extra TreesExplainable K-MeansExplainable K-Nearest NeighborsExplainable LightGBMExplainable Naive BayesExplainable Random ForestExtra TreesGradient BoostingIsolation ForestK-Nearest NeighborsLightGBMLinear Regression (ML)Logistic regression (ML)MARSNaive BayesOnline Decision TreeRandom ForestRegularized Decision TreeRegularized random forestRobust Decision TreeRobust Random ForestRule InductionSelf-supervised Decision TreeSelf-supervised Random ForestSemi-supervised Decision TreeSemi-supervised FP-growthSHAPStackingXGBoost