While the accuracy achieved with the simple classification model is high, it is important to note that misclassifications occur at an overall rate of ~13%. To minimize classification errors, we opted to treat each product as an independent prediction-this was possible because the products were evaluated against multiple ligands, resulting in a multidimensional dataset. This approach enabled us to acquire insights into the prediction uncertainty (39) associated with each individual product, offering a broader understanding of Ullmann C-N coupling reactions. For example, consider product P123 in Fig. , which is anticipated to yield less than 20% according to the classification model. Among the 18 tested ligands, two generated yields exceeding 20% for this specific product. Therefore, the resulting uncertainty, calculated as information entropy ( ) is estimated as 15% for product P123 (see SI for details). The prediction confidence can subsequently be determined as 85% for P123, which is the complementary value of the uncertainty. We then iterated through this process for each product, resulting in a heatmap of prediction confidence. The gaps between known values (products) were interpolated using a radial basis function, which is particularly wellsuited for scattered data and does not necessitate a mesh grid . This methodology yields a comprehensive map where predictions can be made for new products, and the corresponding confidence levels can then be inferred (Fig. ). Through leveraging the error in the initial classification model, we expose the capability of finding potential success even with combinations of substrates considered challenging for Ullmann couplings. For example, combinations that are initially predicted to yield below 20% might still produce desired results if the correct ligand is used.