Simplifying Decision Tree Interpretability with Python & Scikit-learn

When discussing classifiers, decision trees are often thought of as easily interpretable models when compared to numerous more complex classifiers, especially those of the blackbox variety. And this is generally true. This is especially true of rather comparatively simple models created from simple data. This is much-less true of complex decision trees crafted from large amounts of (high-dimensional) data. Even otherwise straightforward decision trees which are of great depth and/or breadth, consisting of heavy branching, can be difficult to trace.