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dask ml dask ml 2025 1 1 documentation dask distributed dask ml examples ecosystem community toggle navigation sidebar get started installation examples use preprocessing cross validation hyper parameter search pipelines and composite estimators generalized linear models naive bayes parallel meta estimators incremental learning clustering api reference dask_ml model_selection train_test_split dask_ml model_selection shufflesplit dask_ml model_selection kfold dask_ml model_selection gridsearchcv dask_ml model_selection randomizedsearchcv dask_ml model_selection incrementalsearchcv dask_ml model_selection hyperbandsearchcv dask_ml model_selection successivehalvingsearchcv dask_ml model_selection inversedecaysearchcv dask_ml ensemble blockwisevotingclassifier dask_ml ensemble blockwisevotingregressor dask_ml linear_model linearregression dask_ml linear_model logisticregression dask_ml linear_model poissonregression dask_ml naive_bayes gaussiannb dask_ml wrappers parallelpostfit dask_ml wrappers incremental dask_ml cluster kmeans dask_ml cluster spectralclustering dask_ml decomposition incrementalpca dask_ml decomposition pca dask_ml decomposition truncatedsvd dask_ml preprocessing standardscaler dask_ml preprocessing robustscaler dask_ml preprocessing minmaxscaler dask_ml preprocessing quantiletransformer dask_ml preprocessing categorizer dask_ml preprocessing dummyencoder dask_ml preprocessing ordinalencoder dask_ml preprocessing labelencoder dask_ml preprocessing polynomialfeatures dask_ml preprocessing blocktransformer dask_ml feature_extraction text countvectorizer dask_ml feature_extraction text hashingvectorizer dask_ml feature_extraction text featurehasher dask_ml compose columntransformer dask_ml compose make_column_transformer dask_ml impute simpleimputer dask_ml metrics mean_absolute_error dask_ml metrics mean_absolute_percentage_error dask_ml metrics mean_squared_error dask_ml metrics mean_squared_log_error dask_ml metrics r2_score dask_ml metrics accuracy_score dask_ml metrics log_loss dask_ml xgboost xgbclassifier dask_ml xgboost xgbregressor dask_ml xgboost train dask_ml xgboost predict dask_ml datasets make_counts dask_ml datasets make_blobs dask_ml datasets make_regression dask_ml datasets make_classification dask_ml datasets make_classification_df integration scikit learn joblib xgboost lightgbm pytorch keras and tensorflow develop changelog contributing dask ml roadmap history theme by the executable book project toggle navigation rst pdf contents dimensions of scale challenge 1 scaling model size challenge 2 scaling data size not everyone needs scalable machine learning scikit learn api partner with other distributed libraries dask ml contents dimensions of scale challenge 1 scaling model size challenge 2 scaling data size not everyone needs scalable machine learning scikit learn api partner with other distributed libraries dask ml dask ml provides scalable machine learning in python using dask alongside popular machine learning libraries like scikit learn xgboost and others you can try dask ml on a small cloud instance by clicking the following button dimensions of scale people may run into scaling challenges along a couple dimensions and dask ml offers tools for addressing each challenge 1 scaling model size the first kind of scaling challenge comes when from your models growing so large or complex that it affects your workflow shown along the vertical axis above under this scaling challenge tasks like model training prediction or evaluation steps will eventually complete they just take too long you ve become compute bound to address these challenges you d continue to use the collections you know and love like the numpy ndarray pandas dataframe or xgboost dmatrix and use a dask cluster to parallelize the workload on many machines the parallelization can occur through one of our integrations like dask s joblib backend to parallelize scikit learn directly or one of dask ml s estimators like our hyper parameter optimizers challenge 2 scaling data size the second type of scaling challenge people face is when their datasets grow larger than ram shown along the horizontal axis above under this scaling challenge even loading the data into numpy or pandas becomes impossible to address these challenges you d use dask s one of dask s high level collections like dask array dask dataframe or dask bag combined with one of dask ml s estimators that are designed to work with dask collections for example you might use dask array and one of our preprocessing estimators in dask_ml preprocessing or one of our ensemble methods in dask_ml ensemble not everyone needs scalable machine learning it s worth emphasizing that not everyone needs scalable machine learning tools like sampling can be effective always plot your learning curve scikit learn api in all cases dask ml endeavors to provide a single unified interface around the familiar numpy pandas and scikit learn apis users familiar with scikit learn should feel at home with dask ml partner with other distributed libraries other machine learning libraries like xgboost already have distributed solutions that work quite well dask ml makes no attempt to re implement these systems instead dask ml makes it easy to use normal dask workflows to prepare and set up data then it deploys xgboost alongside dask and hands the data over from dask_ml xgboost import xgbregressor est xgbregressor est fit train train_labels see dask ml xgboost for more information next installation by dask developers copyright 2017 dask developers