In the field of toxicology, datasets often contain a vast number of variables, including chemical properties, biological activities, and environmental factors. Many of these features may be redundant or irrelevant, leading to overfitting and decreased model accuracy. Effective feature selection helps in: 1. Improving Model Accuracy: By removing irrelevant or noisy features, the model can focus on the most important variables, leading to more accurate predictions. 2. Reducing Overfitting: Simplifying the model by selecting relevant features helps in generalizing better to new, unseen data. 3. Enhancing Interpretability: A model with fewer, more relevant features is easier to understand and interpret, which is crucial for making informed decisions in toxicology.
