Abstract
We demonstrate that the chemical-feature model described in our original paper is distinguishable from the nongeneralizable models introduced by Chuang and Keiser. Furthermore, the chemical-feature model significantly outperforms these models in out-of-sample predictions, justifying the use of chemical featurization from which machine learning models can extract meaningful patterns in the dataset, as originally described.
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CITATION STYLE
Estrada, J. G., Ahneman, D. T., Sheridan, R. P., Dreher, S. D., & Doyle, A. G. (2018). Response to Comment on “Predicting reaction performance in C–N cross-coupling using machine learning.” Science, 362(6416). https://doi.org/10.1126/science.aat8763
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