How to apply BlockwiseVotingClassifier using Dask?

How to apply BlockwiseVotingClassifier using Dask?

How to apply BlockwiseVotingClassifier using Dask?

This recipe helps you apply BlockwiseVotingClassifier using Dask


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]), y)

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