As the first step to constructing a balanced and informative dataset, we assigned negative labels to reactions where the transformation either did not occur or resulted in unknown products beyond the scope of our study. This approach allowed us to simplify the modeling challenge to a binary classification task, focusing solely on predicting substrate reactivity under a fixed set of reaction conditions. This strategy avoided the complexities associated with predicting novel products and was sufficient to enable the machine learning model to focus on learning patterns relevant to the specific type of electrochemical transformation in this study (Figure ). We acknowledge that while predetermined conditions generally work for a number of known reactants, they could yield low outcomes during screening. However, we designated yield optimization as a separate task later in this study to streamline the initial reactivity screening process and avoid the need to tune numerous parameters of electrochemical reactions during this phase. The deployment of this standardized and low-cost rapid screening electrochemical reactor allowed rapid acquisition of a 335 experimental electrochemical oxidation outcome dataset (Table ). which includes both positive and negative labels.