Random forests have several advantages that make them well-suited for toxicology studies:
Accuracy: They tend to provide high prediction accuracy due to the ensemble approach. Handling Missing Data: Random forests can handle missing data effectively, which is common in toxicological datasets. Feature Importance: They offer insights into the importance of various predictors, aiding in the understanding of key factors influencing toxicity. Non-linear Relationships: Capable of capturing complex, non-linear relationships between predictors and outcomes. Robustness: Less prone to overfitting compared to other machine learning methods.
