title: What does `sample_weight` do to the way a `DecisionTreeClassifier` works in sklearn?
body: <p>I've read from the <a href="https://scikit-learn.org/stable/modules/tree.html#tips-on-practical-use" rel="noreferrer">relevant documentation</a> that :</p>
  <blockquote>
  <p>Class balancing can be done by sampling an equal number of samples from each class, or preferably by normalizing the sum of the sample weights (<code>sample_weight</code>) for each class to the same value.</p>
  </blockquote>
  <p>But, it is still unclear to me how this works.  If I set <code>sample_weight</code> with an array of only two possible values, <code>1</code>'s and <code>2</code>'s, does this mean that the samples with <code>2</code>'s will get sampled twice as often as the samples with <code>1</code>'s when doing the bagging?  I cannot think of a practical example for this.</p>