modelenhancement
Modelenhancement is the process of improving a model's performance, reliability, and applicability beyond its initial version. It involves techniques to increase predictive accuracy, robustness to data shifts, calibration of outputs, interpretability, and computational efficiency across the model's lifecycle.
Enhancement can target data, features, and algorithms. Data improvements include curating higher-quality labeled data, addressing class
Training and optimization strategies such as hyperparameter tuning, early stopping, ensembling (bagging, boosting, stacking), and model
Evaluation relies on validated metrics, cross-validation, and holdout tests to measure improvements fairly. Operational deployment requires
Limitations include diminishing returns, increased computational cost, potential data leakage, and overfitting risks if enhancements are