<p>While this is certainly possible for Decision Trees and AN6U5 did a great job describing how, Random Forests use bundles of little trees that were trained using random subsets of the data and random subsets of the features. Thus each tree is optimal only in that limited setting of features and data. Since there are typically 100s or even 1000s of them, figuring out the context by examining the randomized data is going to be a thankless task. I don't think anyone does it.</p>

<p>However there are importance ranking for the features generated for Random Forests and pretty much all implementations will output them if requested. They turn out to be extremely useful. </p>

<p>Two of the most important ones are MDI (Mean Decrease Impurity) and MDA (Mean Decrease Accuracy). They are described in some detail in chapter 6 of this excellent work: <a href="http://arxiv.org/pdf/1407.7502v3.pdf" rel="nofollow">http://arxiv.org/pdf/1407.7502v3.pdf</a> </p>
How to make Decision Tree rules more understandable?
<p>I'd like to extract useful rules from Decision Trees/Random Forest in order to develop a more applicable way to handle the rules and predictions. So I need an application which makes the rules more understandable.</p>

<p>Any suggestions (e.g. visualizations, validation methods etc) for my purpose?</p>