By alexclowe
Profile datasets for quality issues, design A/B experiments, scaffold scikit-learn/XGBoost pipelines, and evaluate models with bias audits, all with documentation-ready model cards.
Generate a data quality report from a dataset description, flag outliers and biases, suggest transforms
Recommend model architecture, generate scikit-learn or XGBoost template, document assumptions
Plan an A/B test or experiment with sample size, power analysis, and recommended duration
Compute cross-validation, generate confusion matrix, audit feature importance for bias
Dataset profiling expertise — auto-scans for missing values, outliers, class imbalance, correlation issues, and schema drift
Model card generation expertise — auto-generates Model Cards covering intended use, limitations, and training data provenance per the HuggingFace and Mitchell et al. standard
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