<p>If you are specifically looking for classifiers in sklearn, you can have a look at this link : <a href="http://scikit-learn.org/stable/modules/scaling_strategies.html" rel="nofollow noreferrer">Scaling Strategies for large datasets</a>.</p>

<p>Generally, the classifiers do incremental learning on your dataset by creating mini-batches. Here are some link for reference :</p>

<p><strong>Incremental Learning links</strong></p>

<ul>
<li><a href="http://www.ra.cs.uni-tuebingen.de/lehre/ss12/advanced_ml/lecture8.pdf" rel="nofollow noreferrer">Advanced ML lecture on Incremental Learning</a></li>
<li><a href="https://blog.bigml.com/2013/03/12/machine-learning-from-streaming-data-two-problems-two-solutions-two-concerns-and-two-lessons/" rel="nofollow noreferrer">ML on streaming data</a></li>
<li><a href="https://en.wikipedia.org/wiki/Incremental_learning" rel="nofollow noreferrer">Incremental Leanring</a></li>
<li><a href="https://arxiv.org/ftp/arxiv/papers/0709/0709.3965.pdf" rel="nofollow noreferrer">Microsoft paper on Incremental Learning</a></li>
</ul>

<p>You can have a look at these classifiers in SKlearn for more info</p>

<ul>
<li><a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.SGDClassifier.html#sklearn.linear_model.SGDClassifier" rel="nofollow noreferrer">SGD Classifier</a></li>
<li><a href="http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.PassiveAggressiveClassifier.html#sklearn.linear_model.PassiveAggressiveClassifier" rel="nofollow noreferrer">Passive Agrressive Classifier</a></li>
<li><a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.MultinomialNB.html#sklearn.naive_bayes.MultinomialNB" rel="nofollow noreferrer">Multinomial Naive Bayes Incremental Learning</a></li>
<li><a href="http://scikit-learn.org/stable/modules/generated/sklearn.naive_bayes.BernoulliNB.html#sklearn.naive_bayes.BernoulliNB" rel="nofollow noreferrer">BErnoulli Naive Bayes</a></li>
</ul>

<p>If your data is given as a stream during input, you can have a look at <a href="https://spark.apache.org/docs/latest/streaming-programming-guide.html" rel="nofollow noreferrer">Apache Spark Streaming</a> and jump to <a href="https://spark.apache.org/docs/latest/ml-guide.html" rel="nofollow noreferrer">MlLib in Apache Spark</a> for more info.</p>

<p>You can also have a look at <a href="http://scikit-learn.org/stable/modules/generated/sklearn.feature_extraction.FeatureHasher.html#sklearn.feature_extraction.FeatureHasher" rel="nofollow noreferrer">Feature Hasher</a> for large scale feature hashing in sklearn.</p>
Which supervised classifiers in scikit-learn are recommended for large datasets?
<p>There are many supervised classifier algorithms available in scikit-learn but I couldn't find any information about their scaalbility regarding large datasets. I know that for instance, support vector machines don't behave well with huge datasets, but what about others?
Which supervised/semi-supervised classifier algorithms are most suitable for large datasets?</p>