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Use Voting Classifiers

A Voting classifier model combines multiple different models (i.e., sub-estimators) into a single model, which is (ideally) stronger than any of the individual models alone.

Dask provides the software to train individual sub-estimators on different machines in a cluster. This enables users to train more models in parallel than would have been possible on a single machine. Note that users will only observe this benefit if they have a distributed cluster with more resources than their single machine (because sklearn already enables users to parallelize training across cores on a single machine).

What follows is an example of how one would deploy a voting classifier model in dask (using a local cluster).

Dask logo

from sklearn.ensemble import VotingClassifier
from sklearn.linear_model import SGDClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC

import sklearn.datasets

We create a synthetic dataset (with 1000 rows and 20 columns) that we can give to the voting classifier model.

X, y = sklearn.datasets.make_classification(n_samples=1_000, n_features=20)

We specify the VotingClassifier as a list of (name, sub-estimator) tuples. Fitting the VotingClassifier on the data fits each of the sub-estimators in turn. We set the n_jobs argument to be -1, which instructs sklearn to use all available cores (notice that we haven’t used dask).

classifiers = [
    ('sgd', SGDClassifier(max_iter=1000)),
    ('logisticregression', LogisticRegression()),
    ('svc', SVC(gamma='auto')),
clf = VotingClassifier(classifiers, n_jobs=-1)

We call the classifier’s fit method in order to train the classifier.

%time, y)
CPU times: user 13.4 ms, sys: 21 ms, total: 34.4 ms
Wall time: 1 s
VotingClassifier(estimators=[('sgd', SGDClassifier()),
                             ('logisticregression', LogisticRegression()),
                             ('svc', SVC(gamma='auto'))],

Creating a Dask client provides performance and progress metrics via the dashboard. Because Client is given no arugments, its output refers to a local cluster (not a distributed cluster).

We can view the dashboard by clicking the link after running the cell.

import joblib
from distributed import Client

client = Client()



  • Workers: 2
  • Cores: 2
  • Memory: 8.36 GB

To train the voting classifier, we call the classifier’s fit method, but enclosed in joblib’s parallel_backend context manager. This distributes training of sub-estimators acoss the cluster.

with joblib.parallel_backend("dask"):, y)

VotingClassifier(estimators=[('sgd', SGDClassifier()),
                             ('logisticregression', LogisticRegression()),
                             ('svc', SVC(gamma='auto'))],
CPU times: user 75.3 ms, sys: 14.8 ms, total: 90.1 ms
Wall time: 757 ms

Note, that we see no advantage of using dask because we are using a local cluster rather than a distributed cluster and sklearn is already using all my computer’s cores. If we were using a distributed cluster, dask would enable us to take advantage of the multiple machines and train sub-estimators across them.