-
-
Notifications
You must be signed in to change notification settings - Fork 25.9k
DOC Introduce dropdowns in the User Guide #26617
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
Comments
+1000. Thanks a lot for suggesting this, this is super useful! |
Working on 1.10. Decision Trees |
Working on 1.7 Gaussian Processes |
Working on 7.2 Real world datasets, 2.1 Gaussian mixtures, 2.2 Manifold Learning How do I make the images on the doc pages load while building it locally? I am only able to see the image path and not the actual image on the webpage |
Working on 7.1. Toy datasets |
You don't seem to be able to get a permalink to headers with these new dropdowns. Would it be possible to add this? Edit: to expand, you used to be able to get a link to a header by clicking next to it (mouse was next to the header in the screenshot): But this will not work with the new drop downs |
I might think of two solutions (but I have poor understanding in HTML/CSS):
Then, in terms of HTML, we can refer to this section using |
You don't seem to be able to get a permalink to headers with these new dropdowns. Would it be possible to add this?
I don't know how to do this, but I would look to sphinx, rather than HTML, if you are trying to link from other pages. Basically, I would look at how to inject a tag in sphinx.
|
Maybe I don't understand the main issue, but if we are trying to resolve permalinks with Sphinx instead of HTML then this can be done:
|
Working on 2.8. Density Estimation |
Working on 1.7. Gaussian Processes |
Update decomposition.rst with drop downs scikit-learn#26617
starting to work on 1.17. Neural network models (supervised) and 1.8. Cross decomposition |
I'll work on 3.2. Tuning the hyper-parameters of an estimator in #27631 |
Awaiting review on this PR - #27551 |
May I work on Ensemble methods ? as the PR #27174 was closed |
Looks like somebody already submitted a PR for that, @lebaudantoine. Next time feel free to do the same or just mention "Working on ...." |
Are there any tasks left in this issue? |
Starting to work on 3.3. Metrics and scoring: quantifying the quality of predictions! |
Thanks to all those who contributed to this issue. Closing as completed! 🎉 |
Uh oh!
There was an error while loading. Please reload this page.
Describe the issue linked to the documentation
Dropdowns are implemented in #26625. They can help users avoid scrolling trough large pages and can quickly get them access to the content they are interested in.
Suggest a potential alternative/fix
Use dropdowns to hide:
References
,Properties
, etc. See for instance the subsections in 3.3.2.16 Detection error tradeoff (DET);Additionally:
Examples
, as it should stay visible to all users. Make sure that theExamples
section comes right after the main discussion with the least possible folded section in-between.For more information see Contributing to documentation, notably the "Guidelines for writing the User Guide and other reStructuredText documents" dropdown.
This is the list of sub-modules to be addressed:
1.2. Linear and Quadratic Discriminant Analysis1.3. Kernel ridge regression1.6
Nearest Neighbors #279191.8
Cross Decomposition #279161.11
Ensemble Methods #279151.12. Multiclass and multioutput algorithms1.14. Semi-supervised learning1.15. Isotonic regression1.16. Probability calibration1.17
Neural Networks (supervised) #279202.4. Biclustering2.6. Covariance estimation2.7. Novelty and Outlier Detection2.9. Neural network models (unsupervised)DOC Add dropdowns to module 2.9 NN Unsupervised #266733.1
Cross Validation #279213.4. Validation curves: plotting scores to evaluate models4.2. Permutation feature importance5.1. Available Plotting Utilities6.3
Preprocessing Data #279226.4. Imputation of missing values6.5. Unsupervised dimensionality reduction6.6. Random Projection6.7. Kernel Approximation6.8. Pairwise metrics, Affinities and Kernels6.9. Transforming the prediction target (y)7.3. Generated datasets7.4. Loading other datasets8.1. Strategies to scale computationally: bigger data8.2. Computational Performance8.3. Parallelism, resource management, and configuration9.2. Interoperable formats10.1. Inconsistent preprocessing10.2. Data leakageContributors willing to address this issue, please offer one of the above sub-modules per pull request. Remember also to mention on which module you are working on.
Thanks for your help!
The text was updated successfully, but these errors were encountered: