AmirhosseinHonardoust

AmirhosseinHonardoust / KPI-Trap-Lab

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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.

business-metrics calibration classification confusion-matrix cost-sensitive-learning
10
0
100% credibility
Found Feb 23, 2026 at 10 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

This repository hosts a detailed technical article explaining pitfalls in relying on single metrics for evaluating machine learning models and offering practical advice for robust assessments.

How It Works

1
🔍 Discover the Article

You find this helpful guide while searching for why good scores sometimes lead to real-world problems in predictions.

2
📖 Read the Warning Story

You learn how focusing on one success number can quietly make your decisions worse without anyone noticing.

3
💡 Spot the Common Traps

The exciting part: you see clear examples of mistakes like ignoring key decision points or overconfidence that fool simple checks.

4
🪜 Build Your Check Ladder

You follow the layered approach to measure not just averages, but real costs, safety, and long-term reliability.

5
🔍 Run the Simple Audit

You apply the easy checklist to your own work, slicing results and testing what happens at different choices.

6
📋 Create Your Safety Rules

You define clear guardrails and monitoring plans so your team can trust and defend their choices.

🎉 Make Reliable Decisions

Now you ship predictions that truly work in the real world, avoiding hidden failures and celebrating real wins.

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AI-Generated Review

What is KPI-Trap-Lab?

KPI-Trap-Lab is a hands-on lab on GitHub that demonstrates how "improving" metrics like AUC or accuracy can quietly degrade real-world ML decisions, using practical examples from fraud detection and underwriting. It walks you through metric audits, slice checks, cost-sensitive evaluations, threshold tuning, and building defensible decision policies to avoid shipping broken models. Delivered as a detailed Markdown guide, it's like a hands-on lab for data teams wanting robust evaluation stacks without code setup.

Why is it gaining traction?

It stands out with its "Metric Ladder" framework—from diagnostics to monitoring—and a copy-paste KPI Trap Checklist that catches issues like calibration drift or slice regressions before production. Developers grab it for the escape hatches on common pitfalls, like evaluating at operating thresholds instead of averages, turning vague dashboards into actionable policies. In a sea of basic metric tutorials, this focuses on decision quality under constraints.

Who should use this?

ML engineers and data scientists building classification models for high-stakes apps like credit risk, content moderation, or hiring pipelines. Prod teams auditing dashboards for false positives spiking support tickets. Analytics leads enforcing guardrails on model releases.

Verdict

Worth bookmarking for its sharp checklist and audit blueprint, especially as a hands-on lab reference, but at 10 stars and 1.0% credibility, it's raw—treat as a starting point, not battle-tested code. Run its evals on your next model retrain to spot traps early.

(178 words)

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