擅长:python、mysql、java
<p>您可以使用GridSearchCV执行此操作,但需要稍作修改。在parameters字典中,您需要在VotingClassfier对象中使用classfier的键,后跟<code>__</code>,然后使用属性本身,而不是直接指定attrbute。在</p>
<p>看看这个例子</p>
<pre><code>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}
</code></pre>