Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Объяснимое дерево решений× | Логистическая регрессия× | |
|---|---|---|
| Область≠ | Машинное обучение | Статистика исследований |
| Семейство≠ | Machine learning | Process / pipeline |
| Год появления≠ | 1984 (CART); XAI framing formalized 2010s–2020s | 1958 |
| Автор метода≠ | Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J. | David Roxbee Cox |
| Тип≠ | Interpretable supervised learning model | Method |
| Основополагающий источник≠ | Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Другие названия≠ | XDT, interpretable decision tree, rule-based decision tree, transparent decision tree | logit model, binomial logistic regression, LR |
| Связанные≠ | 4 | 3 |
| Сводка≠ | An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
| ScholarGateНабор данных ↗ |
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