title: How does RandomForestClassifier work for classification?
body: <p>I have learned that Sklearn treats multi-class classification problems as a collection of binary problems. Quoting the Sklearn user guide:</p>
  <blockquote>
  <p>In extending a binary metric to multiclass or multilabel problems, the data is treated as a collection of binary problems, one for each class.</p>
  </blockquote>
  <p>So, binary classification models like <code>LogisticRegression</code> or Support vector matrices can support multi-class cases by using either One-vs-One or One-vs-Rest strategies. I wanted to know if that was the case for <code>RandomForestClassifier</code> too? How about other classifiers in Sklearn - are they all used as binary classifiers under the hood when dealing with a multi-class problem?</p>