Comments (7)
It seems like a brief page describing the scalability of different approaches might be in-scope.
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Several of the algorithms mentioned in the documentation are parallel implementations on large datasets. See http://dask-ml.readthedocs.io/en/latest/
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Can we please explicitly list them here? Are the GLMs a custom implementation in dask? Future plans?
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Can we please explicitly list them here?
I'm not sure I undestand the question. They are all listed in the documentation. Perhaps you want the API reference? http://dask-ml.readthedocs.io/en/latest/modules/api.html
If you want to list them here that would be fine with me.
Are the GLMs a custom implementation in dask?
Yes. This is already answered in the documentation. See http://dask-ml.readthedocs.io/en/latest/glm.html
If the documentation is not sufficiently clear it would be great if you would collaborate to improve it.
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Sorry, I was typing quickly to finish my thoughts before a call and didn't do a good job. I think I'm asking a question that wouldn't necessarily be expected to be covered by the docs.
What I meant was: What are all the custom algorithm implementations from scratch in dask-ml? I picked up from the docs that GLMs are indeed a custom implementation from scratch. Others (?):
- Preprocessors
- Spectral clustering
The rest seem to wrap existing implementations in some way to better parallelize them, e.g., for parameter search or CV. Is that a correct characterization?
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I think that a comparasion of the different approaches in the same way of the Spectral Clustering Example would be great. It could answer questions like: "how well is dask grid search implementation when compared to sklearn?"
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It looks like @TomAugspurger is doing some work to describe scalability here: #111
I encourage people to engage on that PR
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Related Issues (20)
- 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
- Area under the receiving operating characteristic curve (AUROC) calculation. HOT 2
- The latest version doesn't support perceptron model
- sklearn StandardScaler vs dask StandardScaler. HOT 1
- Nearest Neighbors
- `TypeError` when predicting non-array data with `dask-expr` HOT 6
- Undeclared runtime dependency on setuptools HOT 1
- Documentation on PCA expected max memory usage HOT 1
- Support GPU-backed data for metrics.
- Unexpected behavior in train_test_split with shuffle=False
- ColumnTransformer does not work with Dask dataframes HOT 1
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