https://twitter.com/svpino/status/1706641599017411064
The machine learning drawing feature in human-learn can also be used as an outlier detection model. Checkout calmcode.
A lot of people know of PCA for it's ability to reduce the dimensionality of a dataset. It can turn a wide dataset into a thin one, while hopefully only a limited amount of information. But what about doing it the other way around as well? Can you turn the thin representation into a wide one again? And if so, what might be a use-case for that? → video
PCAOutlierDetection package offers a few algorithms that might help you find outliers.
→ scikit-lego docu
Check-out this tweet
