방법 비교
선택한 방법을 나란히 검토하세요. 서로 다른 행은 강조 표시됩니다.
| 선형 회귀 (ML)× | 로지스틱 회귀 (ML)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1805–1809 | 1958 |
| 창시자≠ | Legendre, A.-M. & Gauss, C.F. | Cox, D. R. |
| 유형≠ | Supervised regression | Probabilistic linear classifier |
| 원전≠ | Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7 | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭 | ordinary least squares regression, OLS, least squares regression, multiple linear regression | logit model, logit regression, binomial logistic regression, maximum entropy classifier |
| 관련 | 5 | 5 |
| 요약≠ | Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task. | 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. |
| ScholarGate데이터셋 ↗ |
|
|