विधियों की तुलना करें
चुनी हुई विधियों की आमने-सामने समीक्षा करें; भिन्नता वाली पंक्तियाँ रेखांकित हैं।
| ग्रेडिएंट बूस्टिंग× | आइसोलेशन फ़ॉरेस्ट× | |
|---|---|---|
| क्षेत्र | मशीन अधिगम | मशीन अधिगम |
| परिवार | Machine learning | Machine learning |
| उद्भव वर्ष≠ | 2001 | 2008 |
| प्रवर्तक≠ | Friedman, J. H. | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| प्रकार≠ | Ensemble (sequential boosting of decision trees) | Unsupervised ensemble (random partitioning trees) |
| मौलिक स्रोत≠ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| उपनाम≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| संबंधित | 5 | 5 |
| सारांश≠ | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. | 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. |
| ScholarGateडेटासेट ↗ |
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