Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| Otsustuspuu× | Isolation Forest× | |
|---|---|---|
| Valdkond | Masinõpe | Masinõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 1984 | 2008 |
| Looja≠ | Breiman, Friedman, Olshen & Stone | Liu, F.T., Ting, K.M. & Zhou, Z.-H. |
| Tüüp≠ | Recursive partitioning (if-then rules) | Unsupervised ensemble (random partitioning trees) |
| Algallikas≠ | 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 ↗ |
| Rööpnimetused≠ | Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection |
| Seotud | 5 | 5 |
| Kokkuvõte≠ | 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. |
| ScholarGateAndmestik ↗ |
|
|