Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Isolation Forest× | Calibración del modelo× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2008 | 2017 |
| Autor original≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Platt; Guo et al. |
| Tipo≠ | Unsupervised ensemble (random partitioning trees) | Post-hoc probability correction technique |
| Fuente seminal≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Guo, C., Pleiss, G., Sun, Y., & Weinberger, K. Q. (2017). On calibration of modern neural networks. International Conference on Machine Learning, 1321–1330. link ↗ |
| Alias≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Classifier Calibration, Probability Calibration, Score Calibration, Model Kalibrasyonu |
| Relacionados≠ | 5 | 3 |
| Resumen≠ | 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. | Model calibration is a post-hoc technique that adjusts the probability outputs of a trained classifier so that predicted confidence scores match empirical outcome frequencies. A classifier is said to be perfectly calibrated if, among all predictions made with confidence p, exactly a fraction p of them are correct. Systematic miscalibration of modern deep neural networks was rigorously documented by Guo et al. (2017), who showed that networks trained with standard cross-entropy loss tend to be overconfident, and proposed temperature scaling as a simple, effective remedy. |
| ScholarGateConjunto de datos ↗ |
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