Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Koku lēmumu pieņemšana (Decision Tree)× | Isolation Forest× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 1984 | 2008 |
| Autors≠ | Breiman, Friedman, Olshen & Stone | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tips≠ | Recursive partitioning (if-then rules) | Unsupervised ensemble (random partitioning trees) |
| Pirmavots≠ | Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth. DOI ↗ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ |
| Citi nosaukumi≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | 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. | 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. |
| ScholarGateDatu kopa ↗ |
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