For chemistry, HTE data has been noted as a valuable source of data for machine learning algorithms, as it is one of the best ways to generate moderate-to-large scale amounts of data in a parallel fashion. However, this data will also some bias: the reagents chosen by the chemist running the screen, the reaction is known to fail with specific motifs thus those motifs are left out of the dataset, or the simple fact that HTE is limited to the set of synthesizable molecules, which can be thought of as a bias, albeit one that we may want the network to learn or to operate in. As observed in the previous sections, HiTEA is adept at finding areas of bias in datasets, which usually take the form of substrate bias. When using these biased datasets for machine learning, one can either a) augment the dataset with further rounds of HTE or additional datasets or b) take a subset of the dataset that is less noisy and less biased; a removal of outliers. Both tasks can be aided by HiTEA through iterative augmentations or reductions followed by HiTEA. Stable and chemically sound HiTEA results (the removal of the surprising insights) indicate a dataset that is relatively robust, and superior for consistent modeling.