手法を比較
選択した手法を並べて確認できます。異なる行はハイライト表示されます。
| 一般化線形モデル（GLM）× | 一般化加法モデル（GAM）× | |
|---|---|---|
| 分野≠ | 統計学 | 機械学習 |
| 系統≠ | Regression model | Machine learning |
| 提唱年≠ | 1972 | 1986 |
| 提唱者≠ | John A. Nelder & Robert W. M. Wedderburn | Trevor Hastie & Robert Tibshirani |
| 種類≠ | Regression framework | Semi-parametric additive regression model |
| 原典≠ | Nelder, J. A., & Wedderburn, R. W. M. (1972). Generalized linear models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370–384. DOI ↗ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ |
| 別名 | GLM, generalized regression, exponential family regression, link-function model | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model |
| 関連≠ | 6 | 4 |
| 概要≠ | The Generalized Linear Model is a unified regression framework that extends ordinary linear regression to outcomes from the exponential family — including binary, count, proportion, and continuous positive outcomes. A link function connects the linear predictor to the mean of the response, enabling principled modelling beyond the Gaussian case. | A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response. |
| ScholarGateデータセット ↗ |
|
|