Data Science

Model results can depend significantly on the variables selected, assumptions made, and methods used to transform or prepare data. A data science expert witness may examine feature selection, variable definitions, preprocessing decisions, model specifications, and analytical assumptions to determine how those choices influenced the reported results.

An expert may evaluate training and testing procedures, validation methods, performance metrics, error rates, benchmark comparisons, and the model's behavior on unseen data. The analysis can address whether reported performance accurately reflects the model's predictive capabilities and whether overfitting, data leakage, or other methodological problems affected the results.

Source code, notebooks, scripts, data pipelines, configuration files, and version histories can reveal how raw data was cleaned, transformed, modeled, and converted into reported results. Reviewing these materials can help determine whether an analysis is reproducible, identify computational errors, and establish which methodological choices influenced the final conclusions.