How to apply BlockwiseVotingClassifier using Dask?
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How to apply BlockwiseVotingClassifier using Dask?

How to apply BlockwiseVotingClassifier using Dask?

This recipe helps you apply BlockwiseVotingClassifier using Dask

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Recipe Objective.

How to apply BlockwiseVotingClassifier using Dask.

Blockwise Voting Classifier trains on blocks or partitions created in Dask DataFrames. A cloned version of estimator fits independently on each block of the collection of Dask.

Ensembling of trained models create predictions.

Step 1- Importing Libraries.

We will import Ensemble from dask_ml and linear_model from sklearn.

#! pip install dask_ml import dask_ml.datasets import dask_ml.ensemble import sklearn.linear_model

Step 2- Preparing Dataset

Classifying Dataset into X, y to prepare the Dataset.

X, y = dask_ml.datasets.make_classification(n_samples=100_000,chunks=10_000) subestimator = sklearn.linear_model.RidgeClassifier(random_state=0)

Step 3- Applying Blockwise Voting Classifier.

clf = dask_ml.ensemble.BlockwiseVotingClassifier(subestimator, classes=[0, 1]) clf.fit(X, y)

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