Machine learningMachine learning
正则化逻辑回归
正则化逻辑回归通过在对数似然函数中添加L1（Lasso）、L2（Ridge）或弹性网络惩罚项，扩展了标准逻辑回归。这使得系数向零收缩，并防止过拟合。当您希望在高维或共线性特征空间中获得可解释、稀疏或稳定的系数估计时，它是二元或多项分类的默认选择。
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Method map
The neighbourhood of related methods — select a node to explore.
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来源
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x ↗
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 4, 18). Springer. ISBN: 978-0-387-84857-0
如何引用本页
ScholarGate. (2026, June 3). Regularized Logistic Regression (L1 / L2 / Elastic Net Penalized Binary and Multinomial Classification). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-logistic-regression
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 弹性网络 (Elastic Net)机器学习↔ compare
- 线性判别分析 (LDA)机器学习↔ compare
- 逻辑回归（机器学习）机器学习↔ compare
- 朴素贝叶斯 (Naive Bayes) 是一种快速的概率分类器，它应用贝叶斯定理，同时假设特征在给定类别时是条件独立的机器学习↔ compare
- 正则化线性回归机器学习↔ compare