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[MRG+2] Adding return_std options for models in linear_model/bayes.py #7838
The reason for this pull request appears in a conversation for #4844
What does this implement/fix? Explain your changes.
This is the first of two pull requests. The ultimate goal is to add the MICE imputation algorithm to scikit-learn. To do so, we need sklearn's Bayesian regression algorithms to be able to return standard deviations as well as predictions.
This pull requests adds the option
Any other comments?
Once this is accepted, I will make a pull request that implements MICE using
Previous example renderings:
I wonder if there's a way to visually emphasise the difference in uncertainties between the centre and edges of these plots.
The problem is that it the uncertainty grows linearly. I tried zooming out further, but the plot looks qualitatively the same.
One idea is to have a non-linear function f(x), and then use a polynomial kernel to estimate it. I'll give this a shot.
And yes, I'll put the figures in the fourth quadrant.