Outlier detection with sklearn I've been reading the sklearn documentation on outlier detection, and even the examples provided by the documentation. Once I have fitted my dataset, all I can do is apply the predict function to the estimator in order to find outliers. However, I would like to get the probabilities that the point is an outlier. Can this be done in sklearn? Is there an R package to do it?

I don't even know if there is a theoretical foundation of the outlier detection methods used by sklearn that allows you to give probabilities. If not, what is the criterium that tells you what is an outlier and what is not? Does it consider probabilities or some kind of non-probabilistic scores?

Any piece of information will be appreciated.

Edit

I would like the outlier method to consider the multivariate distribution of the data. I think that univariate detection methods are rather poor.
One of the easiest and quick way would be to plot a 'boxplot' where the "dots" above the upper whisker(T shaoed line) of the boxplot denotes an outlier(refer to the attached image). I hope this was what you are searching for. Do let me know if otherwise, may be I can help.