Over- and underfitting

Mitigation strategy

1. Implement strategic regularization techniques (e.g., L1, L2, Dropout) and dynamically manage model complexity (e.g., varying layer count or feature set size) to navigate the bias-variance trade-off, thereby constraining the capacity to memorize training noise (overfitting) while ensuring sufficient capacity to capture underlying data patterns (underfitting). 2. Employ k-fold cross-validation during the training process in conjunction with early stopping mechanisms. This ensures the model's performance on independent data subsets is robustly evaluated, halting training at the optimal point before generalization performance on the validation set begins to degrade. 3. Maximize the diversity and volume of the training dataset through data augmentation techniques or the collection of additional labeled data, and perform rigorous feature engineering and data cleaning to minimize the impact of noise and outliers that contribute to poor model fit.