The integration of machine learning in toxicology has revolutionized data analysis by enabling the prediction of toxicological outcomes based on large datasets. Algorithms such as random forest, support vector machine (SVM), and neural networks are employed to model complex relationships and enhance the predictive accuracy of toxicological assessments.
