How Can Machine Learning Models Be Validated in Toxicology?
Validation of ML models in toxicology is crucial to ensure their reliability and accuracy. Common validation techniques include:
- Cross-Validation: This technique involves partitioning the dataset into subsets, training the model on some subsets, and validating it on the remaining ones to assess its performance. - External Validation: Validating the model on independent datasets not used during training can provide insights into its generalizability. - Benchmarking: Comparing the performance of the ML model against established toxicological benchmarks and traditional methods can help evaluate its effectiveness. - Sensitivity Analysis: Analyzing how changes in input data affect model predictions can help in understanding the robustness of the model.
