What are the Common Machine Learning Models Used in Toxicology?
Several ML models are commonly employed in toxicology, including:
1. Random Forest: This ensemble learning method is popular for its robustness and high predictive accuracy. It is used in predicting chemical toxicity. 2. Support Vector Machines (SVM): SVM is employed for classification tasks to determine toxic vs. non-toxic compounds. 3. Neural Networks: Deep learning models can capture complex, non-linear relationships in toxicological data, making them ideal for high-dimensional datasets. 4. k-Nearest Neighbors (k-NN): This model is used for classification based on the closest training examples in the feature space. 5. Gradient Boosting Machines (GBM): GBM helps improve prediction accuracy through iterative training of weak learners.
