ML Model Evaluation

Understands core ML metrics: accuracy, precision, recall, F1, ROC-AUC for classification; RMSE, MAE, R² for regression. Conducts cross-validation for generalization assessment. Builds confusion matrix and classification report via scikit-learn.

Knows basic ML metrics: accuracy, precision, recall, F1. Computes metrics for classification and regression models used in LLM system preprocessing pipelines.

Applies advanced evaluation methods: stratified cross-validation, time-series split, nested cross-validation. Evaluates models considering business metrics: lift, gain charts, expected calibration error. Analyzes models for fairness and bias via disaggregated metrics.

Independently conducts comprehensive ML model evaluation: confusion matrix, ROC-AUC, calibration plots. Evaluates auxiliary ML models in LLM pipelines: safety classifiers, intent detectors.

Designs comprehensive evaluation frameworks for ML models: offline metrics, online metrics, business KPIs. Implements automated model validation gates before production deployment. Applies counterfactual analysis and SHAP for deep model diagnostics.

Designs evaluation frameworks for ML components of the LLM ecosystem: cross-validation strategies, statistical significance testing, fairness metrics. Automates regression testing.

Defines model evaluation standards for the data science team. Establishes evaluation checklist for each ML task type. Coordinates model review process and alignment between offline metrics and business outcomes.

Defines ML evaluation standards for the LLM team. Establishes guidelines for auxiliary ML model assessment, threshold selection, and A/B testing methodology.

Shapes ML quality assurance strategy at organizational level. Defines enterprise model validation standards and audit requirements. Publishes evaluation methodologies for industry and shapes thought leadership.

Shapes enterprise ML evaluation strategy. Defines approaches to model quality governance, automated evaluation pipelines, and alignment of ML metrics with business KPIs at organizational scale.