3.3. Other cool dataviz libraries

3.3.1. PyBaobab for much better decision tree visualizations

PyBaobabdt is a wonderful Python package to visualize decision trees using Sankey diagrams. For a tree classifier, each class is represented with a color, and the width of each link (or root) represents the number of samples in each class.

The trees can become as beautiful as you want by controlling the max_depth parameter.

The link to the package in the comments👇

3.3.2. Print the prediction path of a sample

You can visualize the prediction path of a datapoint in a decision tree using the dtreeviz package. Adding one or two plots like below to your analysis will give viewers a better sense of how predictions are made using decision trees.

Link to dtreeviz in the comments👇

Link to dtreeviz: https://bit.ly/3nDk2la

3.3.3. Mlextend - plot decision boundaries of classifiers

One of the most fun things you can do with your classifier is plot its decision boundaries. But, you will quickly realize that the code to generate such a plot is, put mildly, a giant pain in the keyboard.

Fortunately, the mlextend package collapses all that code into a function, so that you can draw decision boundaries of any classifier in a single line of code👇