Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Regresión Logística× | Máquina de Vectores de Soporte (Clasificación)× | |
|---|---|---|
| Campo≠ | Estadística para la investigación | Aprendizaje automático |
| Familia≠ | Process / pipeline | Machine learning |
| Año de origen≠ | 1958 | 1995 |
| Autor original≠ | David Roxbee Cox | Cortes, C. & Vapnik, V. |
| Tipo≠ | Method | Maximum-margin classifier (kernel method) |
| Fuente seminal≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Alias≠ | logit model, binomial logistic regression, LR | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Relacionados≠ | 3 | 5 |
| Resumen≠ | 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. | The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data. |
| ScholarGateConjunto de datos ↗ |
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