Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| Regressão Logística (ML)× | Árvore de Decisão× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 1958 | 1984 |
| Autor original≠ | Cox, D. R. | Breiman, Friedman, Olshen & Stone |
| Tipo≠ | Probabilistic linear classifier | Recursive partitioning (if-then rules) |
| Fonte seminal≠ | Cox, 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 ↗ |
| Outros nomes≠ | logit model, logit regression, binomial logistic regression, maximum entropy classifier | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Relacionados | 5 | 5 |
| Resumo≠ | Logistic 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. |
| ScholarGateConjunto de dados ↗ |
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