Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| Isolation Forest× | Hoofdcomponentenanalyse× | Random Forest× | |
|---|---|---|---|
| Vakgebied | Machine learning | Machine learning | Machine learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2008 | 2002 | 2001 |
| Grondlegger≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. | Jolliffe, I.T. (textbook); Pearson & Hotelling (origins) | Breiman, L. |
| Type≠ | Unsupervised ensemble (random partitioning trees) | Unsupervised dimensionality reduction | Ensemble (bagging of decision trees) |
| Oorspronkelijke bron≠ | Liu, F.T., Ting, K.M. & Zhou, Z.-H. (2008). Isolation Forest. IEEE ICDM, 413–422. DOI ↗ | Jolliffe, I.T. (2002). Principal Component Analysis (2nd ed.). Springer. DOI ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Aliassen≠ | Isolation Forest (Aykırı Değer Tespiti), iForest, isolation forest anomaly detection | Temel Bileşenler Analizi (PCA), PCA, principal components analysis, Karhunen-Loève transform | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Verwant≠ | 5 | 3 | 4 |
| Samenvatting≠ | 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. | Principal Component Analysis (PCA) is an unsupervised dimensionality-reduction method — given its modern textbook treatment by Ian Jolliffe (2002) — that compresses high-dimensional data into fewer dimensions while preserving the maximum possible variance. It re-expresses correlated variables as a small set of uncorrelated principal components ordered by how much of the data's variation each one captures. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
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