Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| Regresia Logistică× | Naive Bayes× | Mașina cu Vectori Suport (Clasificare)× | |
|---|---|---|---|
| Domeniu≠ | Statistică pentru cercetare | Învățare automată | Învățare automată |
| Familie≠ | Process / pipeline | Machine learning | Machine learning |
| Anul apariției≠ | 1958 | 1997 | 1995 |
| Autorul original≠ | David Roxbee Cox | Mitchell, T. M. (textbook treatment) | Cortes, C. & Vapnik, V. |
| Tip≠ | Method | Probabilistic classifier (Bayes' theorem with conditional independence) | Maximum-margin classifier (kernel method) |
| Sursa seminală≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 | Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297. DOI ↗ |
| Denumiri alternative≠ | logit model, binomial logistic regression, LR | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes | Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier |
| Înrudite≠ | 3 | 4 | 5 |
| Rezumat≠ | 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. | Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate. | 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. |
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