Why might a clinical data scientist involved in regulatory submissions opt for a simpler, more interpretable model like logistic regression?
Answer
The regulatory agencies demand clear, defensible evidence for why the model generated its prediction
There is a tension between predictive power and explainability; regulatory agencies often mandate simpler, interpretable models so that clear, defensible evidence can be provided to support the model's predictions.

Related Questions
What is demanded alongside advanced analytical skills for a clinical data scientist?Which programming languages are listed as essential tools for statistical analysis and machine learning development?What specialized knowledge becomes particularly valuable when dealing with unstructured clinical notes or physician observations?Knowledge of which US regulation is paramount for ensuring patient privacy and data security in analysis pipelines?Familiarity with which medical coding systems is often necessary for effective feature engineering?What aspect truly separates a clinical data scientist, according to the text?If a professional intends to work in pharmaceutical research analyzing clinical trials, what specific structural elements are crucial to understand?What is the primary computational challenge for professionals transitioning from core clinical data management roles?What characteristic should aspiring candidates prioritize in their initial portfolio projects over achieving the highest predictive accuracy?Why might a clinical data scientist involved in regulatory submissions opt for a simpler, more interpretable model like logistic regression?What example certification is provided that demonstrates proficiency in managing and interpreting health data?