Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Discriminante Lineal (LDA)× | Naive Bayes× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia≠ | Latent structure | Machine learning |
| Año de origen≠ | 1936 | 1997 |
| Autor original≠ | Fisher, R. A. | Mitchell, T. M. (textbook treatment) |
| Tipo≠ | Supervised dimensionality reduction and linear classifier | Probabilistic classifier (Bayes' theorem with conditional independence) |
| Fuente 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 |
| Alias | LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis | Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes |
| Relacionados | 4 | 4 |
| Resumen≠ | Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning. | 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. |
| ScholarGateConjunto de datos ↗ |
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