Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Isolation Forest× | Koku lēmumu pieņemšana (Decision Tree)× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2008 | 1984 |
| Autors≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Breiman, Friedman, Olshen & Stone |
| Tips≠ | Unsupervised ensemble (random partitioning trees) | Recursive partitioning (if-then rules) |
| Pirmavots≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ |
| Citi nosaukumi≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf. |
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