Cook's distance outlier detector
A supervised regression outlier detector
Cook's distance can be used to identify points that negatively affect a regression model. A combination of each observation’s leverage and residual values are used in the measurement. Higher leverage and residuals relate to higher Cook’s distances. Read more in the :cite:cook1977outlier --> https://www.jstor.org/stable/1268249
The script cd.py has been added to pyod/models/ containing the Cook's distance outlier detector. The code is mostly based off what has been implemented in the Yellowbrick repo but thought it would be nice to be able to call it with all the others outlier detectors in Pyod.
Hopefully this will become an useful addition to the already great repo and python package that Pyod is.