<h3 dir="auto">Describe the workflow you want to enable</h3>
<p dir="auto">Allow passing trained models to <code class="notranslate">VotingClassifier</code>, and use these trained models directly for prediction, without refitting.</p>
<p dir="auto">The current <code class="notranslate">VotingClassifier</code> requires fitting all inputted estimators on the given training data, even if these estimators have already been trained. This is inconvenient if we want to create an ensemble classifier from estimators trained with different datasets (or with different partitions of the dataset).</p>
<h3 dir="auto">Describe your proposed solution</h3>
<p dir="auto">Maybe adding a new <code class="notranslate">prefit</code> parameter to <code class="notranslate">VotingClassifier</code>, allow the user to specify whether fitting is needed.</p>
<p dir="auto">A similar solution has been implemented in <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="1104212108" data-permission-text="Title is private" data-url="https://github.com/scikit-learn/scikit-learn/issues/22215" data-hovercard-type="pull_request" data-hovercard-url="/scikit-learn/scikit-learn/pull/22215/hovercard" href="https://github.com/scikit-learn/scikit-learn/pull/22215">#22215</a> for <code class="notranslate">StackingClassifier </code>and <code class="notranslate">StackingRegressor</code></p>
<h3 dir="auto">Additional context</h3>
<p dir="auto">This workflow is also mentioned in <a class="issue-link js-issue-link" data-error-text="Failed to load title" data-id="367075000" data-permission-text="Title is private" data-url="https://github.com/scikit-learn/scikit-learn/issues/12297" data-hovercard-type="issue" data-hovercard-url="/scikit-learn/scikit-learn/issues/12297/hovercard" href="https://github.com/scikit-learn/scikit-learn/issues/12297">#12297</a>, as there seems to be no update, here I am requesting it as a new feature.</p>
<p dir="auto">This feature can be implemented at the user-level, code from <a href="https://stackoverflow.com/questions/42920148/using-sklearn-voting-ensemble-with-partial-fit" rel="nofollow">stackoverflow, mors</a></p>
<div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="from sklearn.ensemble import VotingClassifier
from sklearn.preprocessing import LabelEncoder

clf_list = [clf1, clf2, clf3]

eclf = VotingClassifier(estimators = [('1' ,clf1), ('2', clf2), ('3', clf3)], voting='soft')

eclf.estimators_ = clf_list
eclf.le_ = LabelEncoder().fit(y)
eclf.classes_ = seclf.le_.classes_

# Now it will work without calling fit
eclf.predict(X,y)"><pre class="notranslate"><code class="notranslate">from sklearn.ensemble import VotingClassifier
from sklearn.preprocessing import LabelEncoder

clf_list = [clf1, clf2, clf3]

eclf = VotingClassifier(estimators = [('1' ,clf1), ('2', clf2), ('3', clf3)], voting='soft')

eclf.estimators_ = clf_list
eclf.le_ = LabelEncoder().fit(y)
eclf.classes_ = seclf.le_.classes_

# Now it will work without calling fit
eclf.predict(X,y)
</code></pre></div>
<p dir="auto">If this feature is approved, I can work on it.</p>
<p dir="auto">I work on python 3.6.5 and sklearn 0.19.1 on conda 4.5.11.</p>
<p dir="auto">According to this <a href="https://stackoverflow.com/questions/42920148/using-sklearn-voting-ensemble-with-partial-fit" rel="nofollow">post</a>, I am surprised that voting classifiers do not accept <code class="notranslate">prefix</code> classifiers.</p>
<p dir="auto">May I know is it because it is unnecessary from your respective or it was purely short of hand?<br>
Thank you very much.</p>