With dtreeviz, you can visualize how the feature space is split up at decision nodes, how the training samples get distributed in leaf nodes and how the tree makes predictions for a specific observation. These operations are critical to for understanding how classification or regression decision trees work. See article How to visualize decision trees. The scikit-learn Random Forest feature importance and R's default Random Forest feature importance strategies are biased. To get reliable results in Python, use permutation importance, provided here and in our rfpimp package (via pip). A simple Python data-structure visualization tool that started out as a List Of Lists (lol) visualizer but now handles arbitrary object graphs, including function call stacks!

The scikit tree does a good job of representing the tree structure, but we have a few quibbles. The colors aren't the best and it's not immediately obvious why some of the nodes are colored and some aren't. If the colors represent predicted class for this classifier, one would think just the leaves would be colored because only leaves have predictions. The count of samples of the various target classes in each node is somewhat useful, but a histogram would be even better. A target class color legend would be nice.