Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Análisis Discriminante Lineal (LDA× | Vecinos más cercanos (K-NN)× | |
|---|---|---|
| Campo≠ | Estadística | Aprendizaje automático |
| Familia≠ | Hypothesis test | Machine learning |
| Año de origen≠ | 1936 | 1967 |
| Autor original≠ | Ronald A. Fisher | Cover, T.M. & Hart, P.E. |
| Tipo≠ | Parametric linear classifier / dimensionality reduction | Instance-based (non-parametric) learning |
| Fuente seminal≠ | Fisher, R.A. (1936). The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics, 7(2), 179–188. DOI ↗ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ |
| Alias≠ | LDA, Fisher's LDA, Fisher's linear discriminant, discriminant function analysis | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning |
| Relacionados≠ | 7 | 5 |
| Resumen≠ | 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. | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. |
| ScholarGateConjunto de datos ↗ |
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