Statistical rigor is fundamental to model reliability - proper diagnostic procedures ensure models perform consistently in production environments

Model selection balances metrics: ROC-AUC shows discrimination ability, while F1 score highlights precision–recall trade-offs.

Class imbalance is common in real data techniques like SMOTE improve minority class prediction, enabling more accurate and reliable business outcomes

Remediation strategies turn flawed models into reliable predictors; knowing when and how to apply them distinguishes skilled analysts from novices