decision trees from features of multiple datatypes
<p>I'm trying to construct a decision tree with scikit-learn's <code>DecisionTreeClassifier</code>. My data has numeric features consisting of integers and float values.</p>

<p>When constructing the decision tree, the integer features get converted 
to float. </p>

<p>For eg: if A is a feature that can only have integer values from 1-12, splitting criterion such as "A &lt; 5.5" or "A &lt; 3.1" come up in the tree. I don't want float value splitting criteria for A.</p>

<p>The depth of the tree increases if integer features get converted to float. How do I restrict integer features being converted to float?</p>

<p>Also, scikit-learn's <code>DecisionTreeClassifier</code> doesn't allow categorical features. Are there any alternative packages/libraries for constructing decision trees which allow Categorical Features?</p>