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| ロジスティック回帰 (ML)× | 正則化ロジスティック回帰× | |
|---|---|---|
| 分野 | 機械学習 | 機械学習 |
| 系統 | Machine learning | Machine learning |
| 提唱年≠ | 1958 | 1996–2005 |
| 提唱者≠ | Cox, D. R. | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) |
| 種類≠ | Probabilistic linear classifier | Penalized classification model |
| 原典≠ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 別名 | logit model, logit regression, binomial logistic regression, maximum entropy classifier | penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression |
| 関連 | 5 | 5 |
| 概要≠ | Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation. | Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces. |
| ScholarGateデータセット ↗ |
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