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Logistička regresija (ML)×Stablo odlučivanja×
PodručjeStrojno učenjeStrojno učenje
ObiteljMachine learningMachine learning
Godina nastanka19581984
TvoracCox, D. R.Breiman, Friedman, Olshen & Stone
VrstaProbabilistic linear classifierRecursive partitioning (if-then rules)
Temeljni izvorCox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗
Drugi nazivilogit model, logit regression, binomial logistic regression, maximum entropy classifierKarar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Srodne55
SažetakLogistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation.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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ScholarGateUsporedite metode: Logistic regression (ML) · Decision Tree. Preuzeto 2026-06-17 s https://scholargate.app/hr/compare