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| K-최근접 이웃× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 머신러닝 | 연구 통계 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 1967 | 1958 |
| 창시자≠ | Cover, T.M. & Hart, P.E. | David Roxbee Cox |
| 유형≠ | Instance-based (non-parametric) learning | Method |
| 원전≠ | Cover, T.M. & Hart, P.E. (1967). Nearest Neighbor Pattern Classification. IEEE Transactions on Information Theory, 13(1), 21–27. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭≠ | KNN, K-En Yakın Komşu (KNN), nearest neighbor classifier, instance-based learning | logit model, binomial logistic regression, LR |
| 관련≠ | 5 | 3 |
| 요약≠ | K-Nearest Neighbors (KNN), formalized by Cover and Hart in 1967, is a non-parametric, instance-based method that classifies or predicts a new observation by looking at the k closest examples in the training data. For classification it takes a majority vote among those neighbors; for regression it averages their values. | 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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