AmirhosseinHonardoust/KPI-Trap-Lab
A hands-on lab showing how “improving” a single metric (AUC/accuracy/F1) can worsen real-world outcomes. Includes metric audits, slice checks, cost-sensitive evaluation, threshold tuning, and decision policies you can defend, so dashboards don’t quietly ship bad decisions.
This lab helps data science and analytics professionals avoid the 'KPI Trap,' where a model's performance on a single metric (like AUC or accuracy) improves, but real-world business outcomes worsen. It provides practical methods for auditing model metrics, checking performance on specific data segments, and incorporating business costs and decision policies. The output is a robust evaluation framework and defensible decision policies for your models, preventing unexpected issues in production.
Use this if you are a data scientist, analyst, or machine learning engineer responsible for deploying models and need to ensure that metric improvements translate to positive real-world impact and prevent hidden failures.
Not ideal if you are solely focused on early-stage model development and research metrics without considering the model's operational performance and business implications.
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MIT
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Last pushed
Feb 22, 2026
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