Difference between min_samples_split and min_samples_leaf in sklearn DecisionTreeClassifier : <p>I was going through sklearn class <a href="http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html" rel="noreferrer">DecisionTreeClassifier</a>.</p>

<p>Looking at parameters for the class, we have two parameters <strong>min_samples_split</strong> and <strong>min_samples_leaf</strong>. Basic idea behind them looks similar, you specify a minimum number of samples required to decide a node to be leaf or split further. </p>

<p>Why do we need two parameters when one implies the other?. Is there any reason or scenario which distinguish them?. </p>
