OD is Outlier Detection a.k.a anomaly detcetion

PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as Outlier Detection <https://en.wikipedia.org/wiki/Anomaly_detection>_

Since 2017, PyOD has been successfully used in various academic researches

PyOD is a comprehensive Python toolkit to identify outlying objects in multivariate data with both unsupervised and supervised approaches. The model covered in this example includes: All datasets are splitted 60% for training and 40% for testing.

1.Liner approach for outlier detection

a) PCA: Principal Component Analysis use the sum of weighted projected distances to the eigenvector hyperplane as the outlier outlier scores) [[https://www.learnopencv.com/principal-component-analysis/]](https://www.learnopencv.com/principal-component-analysis/])

b) MCD: Minimum Covariance Determinant (use the mahalanobis distances as the outlier scores)

c) OCSVM: One-Class Support Vector Machines

2.Proximity based outlier detection models

a) LOF: Local Outlier Factor

b) CBLOF: Clustering-Based Local Outlier Factor

c) kNN: k Nearest Neighbors (use the distance to the kth nearest neighbor as the outlier score)

d) Median kNN Outlier Detection (use the median distance to k nearest neighbors as the outlier score)

e) HBOS: Histogram-based Outlier Score

3.Probabilistic based outlier detection models

a) ABOD: Angle Based outlier detection

4.Outlier ensembles and combination frameworks

a) Isolation Forest

b) Feature Bagging