Machine learningMachine learning
逻辑回归（机器学习）
逻辑回归是一种基础的概率分类器，它将二元（或多元）结果的对数几率建模为预测变量的线性函数。该方法由 D. R. Cox 于 1958 年提出，至今仍是统计学和机器学习中最广泛使用且最易于解释的分类方法之一，因其校准的概率输出和清晰的系数解释而备受推崇。
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来源
- Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI: 10.1111/j.2517-6161.1958.tb00292.x ↗
- James, G., Witten, D., Hastie, T. & Tibshirani, R. (2013). An Introduction to Statistical Learning (Ch. 4). Springer. ISBN: 978-1-4614-7138-7
如何引用本页
ScholarGate. (2026, June 3). Logistic Regression (Machine Learning Classification Model). ScholarGate. https://scholargate.app/zh/machine-learning/logistic-regression-ml
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.
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