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| 孤立森林 (Isolation Forest)× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 研究统计学 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2008 | 1958 |
| 提出者≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | David Roxbee Cox |
| 类型≠ | Unsupervised ensemble (random partitioning trees) | Method |
| 开创性文献≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. 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 | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | logit model, binomial logistic regression, LR |
| 相关≠ | 5 | 3 |
| 摘要≠ | Isolation Forest is an unsupervised machine-learning method for anomaly and outlier detection, introduced by Liu, Ting and Zhou in 2008, that isolates anomalies through random partitioning of the data. It works without any labelled anomaly data and scales to high-dimensional datasets. | 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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