Support¶
There are several ways to get in touch with the developers.
Mailing List¶
The main mailing list is scikit-learn.
There is also a commit list scikit-learn-commits, where updates to the main repository and test failures get notified.
User questions¶
Some scikit-learn developers support users on StackOverflow using the [scikit-learn] tag.
For general theoretical or methodological Machine Learning questions stack exchange is probably a more suitable venue.
In both cases please use a descriptive question in the title field (e.g. no “Please help with scikit-learn!” as this is not a question) and put details on what you tried to achieve, what were the expected results and what you observed instead in the details field.
Code and data snippets are welcome. Minimalistic (up to ~20 lines long) reproduction script very helpful.
Please describe the nature of your data and how you preprocessed it:
what is the number of samples, what is the number and type of features
(i.d. categorical or numerical) and for supervised learning tasks,
what target are your trying to predict: binary, multiclass (1 out of
n_classes) or multilabel (k out of n_classes) classification
or continuous variable regression.
User questions should not be asked on the bug tracker, as it crowds the list of issues and makes the development of the project harder.
Bug tracker¶
If you think you’ve encountered a bug, please report it to the issue tracker:
https://github.com/scikit-learn/scikit-learn/issues
Don’t forget to include:
steps (or better script) to reproduce,
expected outcome,
observed outcome or Python (or gdb) tracebacks
To help developers fix your bug faster, please link to a https://gist.github.com
holding a standalone minimalistic python script that reproduces your bug and
optionally a minimalistic subsample of your dataset (for instance, exported
as CSV files using numpy.savetxt).
Note: Gists are Git cloneable repositories and thus you can use Git to push datafiles to them.
Gitter¶
Some developers like to hang out on scikit-learn Gitter room: https://gitter.im/scikit-learn/scikit-learn.
Documentation resources¶
This documentation is relative to 1.3.2. Documentation for other versions can be found here.
Printable pdf documentation for old versions can be found here.