treeheatr and pmlbr: visualizing decision trees on benchmark datasets

The presentation of the decision tree with data represented as a heatmap is a new visualization that uncovers the tree's performance, the data's correlation structure, and the importance of each feature in predicting the outcome. Implemented in an easily installed package with a detailed vignette, treeheatr can be a useful teaching tool to enhance students’ understanding of a simple decision tree model before diving into more complex tree-based machine learning methods. We will apply decision tree models and visualize them on multiple benchmark machine learning datasets in pmlbr.

About our speaker:
Trang Le is a postdoctoral fellow with Jason Moore at the Computational Genetics Lab, University of Pennsylvania. She enjoys developing machine learning methods for rigorous analyses of a wide array of biomedical data. Most recently, her work focuses on investigating the long-term effect of neurological conditions in COVID-19 patients. She’s the author and maintainer of multiple R packages.