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| Anàlisi Discriminant Lineal (LDA× | Naive Bayes× | |
|---|---|---|
| Camp≠ | Estadística | Aprenentatge automàtic |
| Família≠ | Hypothesis test | Machine learning |
| Any d'origen≠ | 1936 | 1997 |
| Autor original≠ | Ronald A. Fisher | Mitchell, T. M. (textbook treatment) |
| Tipus≠ | Parametric linear classifier / dimensionality reduction | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Font seminal≠ | Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072 |
| Àlies | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Relacionats≠ | 7 | 4 |
| Resum≠ | Linear Discriminant Analysis (LDA) is a parametric supervised classification method that finds the linear combination of continuous predictors that best separates two or more predefined groups. Introduced by Ronald A. Fisher in his landmark 1936 paper on taxonomic measurements, it simultaneously serves as a classifier and a dimensionality-reduction tool, and can be understood as the classification-oriented counterpart of MANOVA. | 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. |
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