Comments (3)
The separation between single machine and distributed learning seems odd to me.
Agreed. I'm not sure why I found that distinction so relevant a few months ago.
Your proposed TOC sounds reasonable. For now, I think Pipelines
would make a good home for things in preprocessing/
.
I've also found it pleasant recently to start sections with the API relevant for that section.
That could get a bit boring for scikit-learn style estimators, since it'll (almost) always be.fit
, .transform
, etc :) Perhaps I'm misunderstanding though.
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And if you're busy, I'll have time to work on this later today or tomorrow, as I wait around for pandas release things to finish.
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That could get a bit boring for scikit-learn style estimators, since it'll (almost) always be.fit, .transform, etc :) Perhaps I'm misunderstanding though.
Yeah, to be clear I'm saying that we just add the .. autosummary:
line at the top of each relevant section. When you go to the Generalized Linear Models section you see LinearRegression, LogisticRegression, PoissonRegression, _GLM at the top of the section. If you care to you can click on those to be taken to the full API docs on another page.
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Related Issues (20)
- Logistic Regression Fails with ValueError: Shapes Not Aligned HOT 1
- Better error message when using invalid `MinMaxScaler.fit(...)` inputs
- Default datatype when using CountVectorizer and HashingVectorizer should be sparse COO
- LabelEncoder raises errors with string and string[pyarrow] types HOT 1
- LabelEncoder doesn't handle missing values in *dask* series of strings HOT 3
- can't set attribute error when running PCA
- KFold cross validation fails with dask dataframes HOT 2
- Mistake.
- Add backward compatibility for supported version of scikit-learn
- Bug in ColumnTransformer HOT 2
- HashingVectorizer behaves differently from FeatureHasher HOT 1
- sklearn handles text labels differently than ml_dask on OneHotEncoding
- Implementation for make_s_curve HOT 2
- Import dask_ml with python 3.10 failed due to conflict with dask.distributed HOT 4
- Python 3.11 support HOT 2
- LogisticRegression.score returns an empty dask array
- Incremental does not handle dask arrays of ndim>2 in estimator training HOT 2
- loading dask_ml gives error contextualversionconflict with sklearn HOT 4
- For a single record data frame train_test_split() sometimes assigns this single record to test set. HOT 2
- The `log_loss`-function crashes when using mixed types
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