方法对比
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| 普通最小二乘法 (OLS) 回归× | Lasso 回归× | 逻辑回归× | |
|---|---|---|---|
| 领域≠ | 计量经济学 | 机器学习 | 研究统计学 |
| 方法族≠ | Regression model | Machine learning | Process / pipeline |
| 起源年份≠ | 2019 | 1996 | 1958 |
| 提出者≠ | Wooldridge (textbook treatment); classical least squares | Tibshirani, R. | David Roxbee Cox |
| 类型≠ | Linear regression | Regularized linear regression (L1 penalty) | Method |
| 开创性文献≠ | Wooldridge, J. M. (2019). Introductory Econometrics: A Modern Approach (7th ed.). Cengage Learning. ISBN: 978-1337558860 | Tibshirani, R. (1996). Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 别名≠ | ordinary least squares, classical linear regression, linear regression, en küçük kareler regresyonu | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | logit model, binomial logistic regression, LR |
| 相关≠ | 5 | 4 | 3 |
| 摘要≠ | Ordinary Least Squares is the classical linear regression method that explains a continuous outcome as a linear combination of predictors. It estimates the coefficients by minimising the sum of squared residuals, and under the Gauss-Markov assumptions these estimates are the best linear unbiased estimator (BLUE). | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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