Arturo Amor
Arturo Amor
I am currently working on the [Adjustment for chance in clustering performance evaluation](https://scikit-learn.org/dev/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py) example in https://github.com/scikit-learn/scikit-learn/pull/23708. It would be natural to make the modifications there. I use the default `matplotlib`...
I think our work may be complementary, as the only thing I've done so far in https://github.com/scikit-learn/scikit-learn/pull/24099 is to fix the conflicts with `main`. Feel free to fetch the last...
> @jmloyola and @ArturoAmorQ: would you be interested in reviewing this contribution? I think this can be a way to get you started with Sphinx, building the documentation and reStructured...
As this work was stalled for one year, I just opened a PR intending to carry it on.
I would add - [x] 5. Add discussion/plots on score distributions - [x] 6. Add a figure similar to [this one](https://scikit-learn.org/stable/_images/grid_search_cross_validation.png) illustrating cross-validation. Good enough for now.
My idea / proposition for points 5. and 6. Notice that the distribution plot matches the example.  
If I understand correctly, #420 proposes directing the search results links towards .github.io. That might be the easiest solution in both cases. > This search tab could also be a...
Worst case scenario, concepts that are not covered in the [user guide](https://scikit-learn.org/stable/user_guide.html) could be linked to wikipedia, what do you think?
Addressed in [!72](https://gitlab.inria.fr/learninglab/mooc-scikit-learn/mooc-scikit-learn-coordination/-/merge_requests/72) (not yet merged).
> We should then present intuitively the concept of formal "significance" with overlapping score distributions of two models. Piece of code to address this ```python import pandas as pd import...