Diagnostic reproduction of analyses from [1], with implementation of Shapely framework (SHAP [2]) for generating explanatory statistics. Demonstrated are various adhoc analyses enabled by using the "effect matrix" generated by SHAP as synthetic data for various purposes, including hypothesis testing, supervised clustering, and analytical visualizations. We also demonstrate equivalence of SHAP as synthetic data (wrt nominal or scaled data) by using it to predict yield via regression and RandomForest, and compare to the original implementations of the same in [1]. 1. Ahneman, Derek T., Jesús G. Estrada, Shishi Lin, Spencer D. Dreher, and Abigail G. Doyle. “Predicting Reaction Performance in C–N Cross-Coupling Using Machine Learning.” Science, February 15, 2018. https://doi.org/10.1126/science.aar5169. 2. Lundberg, Scott M., Gabriel Erion, Hugh Chen, Alex DeGrave, Jordan M. Prutkin, Bala Nair, Ronit Katz, Jonathan Himmelfarb, Nisha Bansal, and Su-In Lee. “From Local Explanations to Global Understanding with Explainable AI for Trees.” Nature Machine Intelligence 2, no. 1 (January 2020): 56–67. https://doi.org/10.1038/s42256-019-0138-9.

Output DFT intensive course