So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| Chẩn đoán ảnh hưởng (Khoảng cách Cook, DFFITS, Đòn bẩy)× | Phân tích thành phần chính× | |
|---|---|---|
| Lĩnh vực≠ | Thống kê | Học máy |
| Họ≠ | Regression model | Machine learning |
| Năm ra đời≠ | 1977 | 2002 |
| Người khởi xướng≠ | R. Dennis Cook (Cook's distance); Belsley, Kuh & Welsch (DFFITS, leverage) | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Loại≠ | Regression diagnostic | Unsupervised dimensionality reduction |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác≠ | Cook's distance, DFFITS, leverage, influential observation detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Liên quan≠ | 5 | 3 |
| Tóm tắt≠ | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|