Distill-Then-Detect: A Practical Framework for Error-Aware Machine Learning Even the most sophisticated neural networks and boosting algorithms occasionally falter on a small but crucial segment of data—typically around 10% of validation cases—where prediction errors significantly increase. These "big misses" are often due to the complexity and variability of real-world inputs, such as outliers, peculiar feature combinations, or hidden patterns that the model has not been trained to recognize. Without a reliable method to identify these problematic cases, businesses can face substantial losses. For instance, in credit scoring, misclassifying a few high-risk applicants can lead to significant loan defaults. In manufacturing, an inability to detect the machines on the verge of failure can disrupt entire production lines. To address this issue, I propose a framework called "Distill-Then-Detect," which combines three practical steps to diagnose and flag high-risk predictions. First, I distill a compact "student" model from a more powerful "teacher" model. This process retains the accuracy of the teacher while improving computational efficiency. Second, I quantify the prediction uncertainty of the teacher and train a lightweight meta-model to identify where the teacher is likely to make mistakes. Lastly, I apply a calibrated thresholding method to ensure that most high-risk cases are correctly flagged. Step 1: Distillation of a Compact Student Model Model distillation involves transferring knowledge from a large, complex model (the teacher) to a smaller, simpler model (the student). The goal is to achieve similar performance with reduced computational resources. By using a less resource-intensive student model, we can perform predictions faster and more cost-effectively, making it feasible for real-time applications in industries like finance and manufacturing. Step 2: Quantifying Prediction Uncertainty Prediction uncertainty is a measure of how confident the model is in its predictions. High uncertainty indicates that the model is unsure about the input data, which may suggest the presence of outliers or unusual patterns. To quantify this uncertainty, I use techniques such as Bayesian methods, ensemble learning, or Monte Carlo dropout. Once the uncertainty is quantified, a lightweight meta-model is trained to recognize patterns in the teacher's uncertainty scores and predict where the teacher is likely to make errors. Step 3: Calibrated Thresholding for High-Risk Detection The final step involves setting thresholds to flag high-risk predictions based on the uncertainty scores from the meta-model. This thresholding is calibrated to balance between sensitivity and specificity, ensuring that most high-risk cases are caught without overwhelming the system with false positives. The calibrated approach guarantees that the framework can reliably alert businesses to potential issues, allowing for timely interventions to mitigate risks. Case Study: Credit Scoring In the context of credit scoring, the Distill-Then-Detect framework can be particularly useful. Traditional scoring models often miss identifying high-risk applicants, leading to loan defaults. By distilling a compact student model from a powerful teacher, we ensure that the scoring system remains fast and efficient. The meta-model trained on prediction uncertainty helps identify applicants with unusual financial profiles, such as those with irregular income streams or unique spending patterns. Applying calibrated thresholds, we can flag these high-risk applicants for further review, reducing the likelihood of default. Case Study: Manufacturing In manufacturing, predictive maintenance is critical for preventing machine failures that can disrupt production. The Distill-Then-Detect framework can help by first creating a compact student model that can quickly identify potential issues. The meta-model then assesses the uncertainty of the teacher's predictions to pinpoint machines that are likely to fail due to unusual operational conditions or previously unseen faults. With calibrated thresholds, the system can reliably flag these high-risk machines, enabling preemptive maintenance and minimizing downtime. Conclusion The Distill-Then-Detect framework offers a robust and practical approach to handling high-risk predictions in machine learning. By combining model distillation, uncertainty quantification, and calibrated thresholding, this method ensures that businesses can identify and address problematic cases before they lead to significant losses. Whether in credit scoring or manufacturing, the framework enhances the reliability and efficiency of machine learning systems, making them better suited for real-world applications where precision is paramount.