Poor model accuracy

Mitigation strategy

1. Data Governance and Quality Enhancement Implement rigorous data cleansing, validation, and augmentation protocols, including the systematic treatment of missing and outlier values, to ensure the model is trained on a sufficiently large, diverse, and high-integrity dataset, which is foundational for maximizing predictive accuracy. 2. Advanced Model and Feature Engineering Apply systematic feature engineering (creation, transformation, and selection) to ensure optimal representation of variables. Follow this with a comprehensive model selection process, leveraging techniques such as hyperparameter optimization, cross-validation, and ensemble methods (e.g., Gradient Boosting) to achieve robust performance and generalization. 3. Continuous Performance Monitoring and Bias Mitigation Establish continuous post-deployment monitoring frameworks to detect and address performance degradation (model drift) and accuracy disparities across different subgroups. Employ fairness-aware machine learning techniques and comprehensive evaluation metrics to ensure both the reliability and the equitable performance of the system over time.