Machine learning models were trained to predict reaction outcomes using various data set splitting strategies (Figure ), including a 0D split for known combinations, 1D splits for novel acids or N -arenes, and a 2D split for novel combinations of both components (Figure ). The performance of these models, assessed via reaction yield and binary outcome predictions, highlights their capacity for reaction forward prediction (Methods and SI1). The random split (0D) consistently outperformed the other strategies, with a mean absolute error (MAE) of 6.7% and a Pearson correlation coefficient (r) of 0.83, demonstrating strong predictive power for both reaction yield and binary outcomes (accuracy = 85.7%, precision = 90.9%) (SI1). Extrapolation to novel acids (1DA) and novel N -arenes (1DN) resulted in increased errors, with MAEs of 12.5% and 11.4%, respectively. The 2D split, which tested novel combinations of both acids and N -arenes, showed comparable performance to the 1D splits, with an MAE of 12.6%, retaining relatively high precision at 85.4% (SI1).