Known Issues & Warnings#

This is the central place to track known issues.


There are some known dependency issues/notes. Refer installation for more information.

Neural Networks#

SO_GAAL and MO_GAAL may only work under Python 3.5+.

Differences between PyOD and scikit-learn#

Although PyOD is built on top of scikit-learn and inspired by its API design, some differences should be noted:

  • All models in PyOD follow the tradition that the outlying objects come with higher scores while the normal objects have lower scores. scikit-learn has an inverted design–lower scores stand for outlying objects.

  • PyOD uses “0” to represent inliers and “1” to represent outliers. Differently, scikit-learn returns “-1” for anomalies/outliers and “1” for inliers.

  • Although Isolation Forests, One-class SVM, and Local Outlier Factor are implemented in both PyOD and scikit-learn, users are not advised to mix the use of them, e.g., calling one model from PyOD and another model from scikit-learn. It is recommended to only use one library for consistency (for three models, the PyOD implementation is indeed a set of wrapper functions of scikit-learn).

  • PyOD models may not work with scikit-learn’s check_estimator function. Similarly, scikit-learn models would not work with PyOD’s check_estimator function.