Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Эластичная сеть× | Анализ главных компонент× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2005 | 2002 |
| Автор метода≠ | Zou, H. & Hastie, T. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) |
| Тип≠ | Regularized linear regression (L1 + L2 penalty) | Unsupervised dimensionality reduction |
| Основополагающий источник≠ | Zou, H. & Hastie, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society: Series B, 67(2), 301–320. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ |
| Другие названия | Elastic Net Regresyon, elastic net regression, ElasticNet, L1/L2 regularized regression | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform |
| Связанные≠ | 4 | 3 |
| Сводка≠ | Elastic Net is a regularized linear regression method introduced by Zou and Hastie in 2005 that blends the LASSO (L1) and Ridge (L2) penalties, so it performs variable selection and coefficient shrinkage at the same time. It is designed for predictive and explanatory modelling on data with many, possibly correlated, predictors. | 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. |
| ScholarGateНабор данных ↗ |
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