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| Isolation Forest× | Régression logistique× | |
|---|---|---|
| Domaine≠ | Apprentissage automatique | Statistiques de recherche |
| Famille≠ | Machine learning | Process / pipeline |
| Année d'origine≠ | 2008 | 1958 |
| Auteur d'origine≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | David Roxbee Cox |
| Type≠ | Unsupervised ensemble (random partitioning trees) | Method |
| Source fondatrice≠ | 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 ↗ |
| Alias | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | logit model, binomial logistic regression, LR |
| Apparentées≠ | 5 | 3 |
| Résumé≠ | 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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