-
Notifications
You must be signed in to change notification settings - Fork 45
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
documentation: np.mean(y_pred) does not work #3
Comments
Thanks for flagging this up. You are right, the problem is that I guess the other question is why the abstract Distribution object implements an slightly obscure |
Regarding point vs mean - I think this is a valid point. I mean, no pun intended, it's just the means to point this out. However, I personally think that all continuous distributions which support it should have a mean for the reason that this is an obvious method one would be looking for, especially if one is familiar with the scipy.stats distributions, see e.g., More generally to this point: as @murphyk points out in separate communication, for future development one might consider using scipy.stats distributions as return objects - similar to https://github.com/alan-turing-institute/MLJ.jl which uses https://github.com/JuliaStats/Distributions.jl as return objects (upon suggestion based on the skpro design, ahem, |
Fixed in version v1.0.1. On using scipy.stats, how would this look like for non-standard distributions, e.g. the Bayesian case where we only obtain a posterior sample? Can scipy stats represent that? Feel free to open another issue to continue the discussion on that topic. |
I'm following along with this intro example.. However this line fails
Error below (seems to be because Distribution objects don't support the mean() function but instead insist on obscurely calling it
point
!)The text was updated successfully, but these errors were encountered: