The primary goal in toxicology is to predict the harmful effects of substances accurately, often with limited data. Overfitting compromises this ability by leading to models that cannot generalize beyond the dataset they were trained on. This is particularly troubling in toxicology where the consequences of incorrect predictions can be severe, affecting public health and safety. The high variability and complexity of biological data further exacerbate the challenge, making robust model development crucial.
