Comparer des méthodes
Examinez les méthodes sélectionnées côte à côte ; les lignes qui diffèrent sont mises en évidence.
| Diagnostics d'influence (Distance de Cook, DFFITS, Effet de levier)× | Analyse en composantes principales× | |
|---|---|---|
| Domaine≠ | Statistique | Apprentissage automatique |
| Famille≠ | Regression model | Machine learning |
| Année d'origine≠ | 1977 | 2002 |
| Auteur d'origine≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Type≠ | Regression diagnostic | Unsupervised dimensionality reduction |
| Source fondatrice≠ | Cook, R. D. (1977). Detection of Influential Observations in Linear Regression. Technometrics, 19(1), 15-18. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Alias≠ | Cook's distance, DFFITS, leverage, influential observation detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | Influence diagnostics are a family of post-fit measures that quantify how much each single observation affects a fitted regression. Cook's distance was introduced by R. Dennis Cook in 1977, with leverage and DFFITS formalised by Belsley, Kuh and Welsch in 1980, to flag the observations that most strongly pull the estimated coefficients. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. |
| ScholarGateJeu de données ↗ |
|
|