Voting classifier grid search

from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import VotingClassifier
from sklearn.model_selection import GridSearchCV

X = np.array([[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2],[-1.0, -1.0], [-1.2, -1.4], [-3.4, -2.2], [1.1, 1.2]])
y = np.array([1, 1, 2, 2,1, 1, 2, 2])

eclf = VotingClassifier(estimators=[ 
    ('svm', SVC(probability=True)),
    ('lr', LogisticRegression()),
    ], voting='soft')

#Use the key for the classifier followed by __ and the attribute
params = {'lr__C': [1.0, 100.0],
      'svm__C': [2,3,4],}

grid = GridSearchCV(estimator=eclf, param_grid=params, cv=2)

grid.fit(X,y)

print (grid.best_params_)
#{'lr__C': 1.0, 'svm__C': 2}

Source: stackoverflow.com

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