[DOC] add newer patterns to handle unequal length and multivariate data in tutorial #7362
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…ata to classification tutorial
PranavBhatP
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benHeid,
fkiraly,
fnhirwa,
geetu040,
pranavvp16,
XinyuWuu and
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hi @fkiraly, can you review the changes to the tutorial notebook, wanted to see if I should add anymore changes here. |
fkiraly
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Nov 10, 2024
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Nice! I think we could merge this and it would already be a great improvement.
Some smaller comments:
- typographically, names of classes should be enclosed between backticks, like
IndepDist, that way they appear in code font - at the start, could you add a math explanation or reference (or both) to explain the difference?
- for more details on distances and composites, a pointer to notebook 6 might be good
- I would also add a third method: some estimators can deal with multivariate data natively, this is recorded in the capability tag (see section 2.3 below how to search). This should perhaps be the first method you explain.
- the first vignette uses two aggregation functions - sum and mean - which are very similar but double the amount of lines. I would suggest, show only one of these first, and mention how you would switch the aggregation function only.
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Sure! I'll add these changes. |
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@fkiraly , I've added the requested changes. |
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Reference Issues/PRs
Fixes: #7237
What does this implement/fix? Explain your changes.
Adds the new patterns to deal with unequal length and multi-variate data into the classification tutorial.
Does your contribution introduce a new dependency? If yes, which one?
What should a reviewer concentrate their feedback on?
Quality and relevance of added documentation, open to feedback.
Did you add any tests for the change?
No.
Any other comments?
PR checklist
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maintainerstag - do this if you want to become the owner or maintainer of an estimator you added.See here for further details on the algorithm maintainer role.
For new estimators
docs/source/api_reference/taskname.rst, follow the pattern.Examplessection.python_dependenciestag and ensureddependency isolation, see the estimator dependencies guide.