dask-ml
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dask
Scalable Machine Learning with Dask
As this functionality has moved to `xgboost`, drop `dask-xgboost` and update docs to reference the `xgboost` library.
I believe this is related to #521. When using dask-ml with `xgboost.dask.DaskXGBClassifier` and `dask_ml.model_selection.GridSearchCV`, you run into TypeErrors suggesting that the estimator expects `dask.array.core.Array ` types, even while using Dask...
- Pass `loss` to the constructor as documented in https://www.adriangb.com/scikeras/stable/migration.html - Update package versions - Update syntax to use SciKeras compiled models
For Pandas CategoricalDtype(categories=['a', 'b'], ordered=False) categorizer.transform(X2)["x"].dtype # > CategoricalDtype(categories=['b', 'a'], ordered=False) ``` For Pandas>=1.2, the above code snippet produces the same result for `X1` and `X2` (as we would expect)....
**What happened**: I was running GridSearchCV with multiple scoring metrics. One of them ("neg_mean_poisson_deviance") was undefined for some folds b/c it is undefined when y_hat is 0. This was handled...
Add GMM
Hi all, Is there any plan to implement a parallel version of GMM (Gaussian Mixture Modelling) ? Thanks g eg: http://dx.doi.org/10.1109/CSAE.2012.6272849
Hello, I am learning to use Dask ml to analyze data, here is part of my code: `from dask_ml.linear_model import LogisticRegression lr = LogisticRegression(max_iter=1000) lr.fit(x_train_tfidf, y_train)` When I run it,...
Lazy fitting
Should we add a `compute` keyword to all the `fit` / `partial_fit` on estimators we implement? This would aid with - debugging the graphs we build - Better scheduling (any...
