Compara mètodes
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| Naive Bayes semi-supervisat× | Regressió Logística× | |
|---|---|---|
| Camp≠ | Aprenentatge automàtic | Estadística per a la recerca |
| Família≠ | Machine learning | Process / pipeline |
| Any d'origen≠ | 2000 | 1958 |
| Autor original≠ | Nigam, K.; McCallum, A. K.; Thrun, S.; Mitchell, T. | David Roxbee Cox |
| Tipus≠ | Semi-supervised generative classifier | Method |
| Font seminal≠ | Nigam, K., McCallum, A. K., Thrun, S., & Mitchell, T. (2000). Text Classification from Labeled and Unlabeled Documents using EM. Machine Learning, 39(2–3), 103–134. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Àlies≠ | SSL Naive Bayes, EM-Naive Bayes, semi-supervised generative classifier, Nigam et al. text classifier | logit model, binomial logistic regression, LR |
| Relacionats≠ | 4 | 3 |
| Resum≠ | Semi-supervised Naive Bayes extends the classic Naive Bayes generative model to exploit large pools of unlabeled data alongside a small labeled set. Using Expectation-Maximization, it iteratively infers soft class assignments for unlabeled examples and re-estimates class and feature parameters, yielding substantially better classifiers when labeled examples are scarce. | 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. |
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