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。
| 一般化加法モデル（GAM）× | 勾配ブースティング× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1986 | 2001 |
| 提唱者≠ | Trevor Hastie & Robert Tibshirani | Friedman, J. H. |
| 種類≠ | Semi-parametric additive regression model | Ensemble (sequential boosting of decision trees) |
| 原典≠ | Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310. DOI ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| 別名 | GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| 関連≠ | 4 | 5 |
| 概要≠ | 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateデータセット ↗ |
|
|