Designing and Implementing Custom Metrics for AI Projects
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Course Modules:
Module 1: Why Custom Metrics Matter
Limitations of standard metrics (accuracy, F1-score, etc.)
Business-contextual scoring needs
Use cases that demand custom approaches
Module 2: Building Custom Evaluation Functions
Writing scoring functions in Python
Input/output design and integration with Scikit-learn
Unit testing and validation of metric behavior
Module 3: Cost-Sensitive and Risk-Aware Metrics
False positive vs. false negative costs
Utility scoring and expected loss functions
Application: fraud detection, medical diagnosis, etc.
Module 4: Fairness and Ethics-Based Metrics
Group fairness metrics: equal opportunity, predictive parity
Custom equity scores
Designing socially aware performance measures
Module 5: Composite and Weighted Scoring Systems
Creating multi-metric evaluation dashboards
Weighting trade-offs: speed vs. precision, accuracy vs. fairness
Scorecard design and stakeholder review
Module 6: Capstone Project – Create a Custom Metric
Choose a domain-specific challenge (e.g., hiring, lending, education)
Define and implement a new evaluation metric
Submit code, explanation, and comparison against standard metrics
Tools & Technologies Used:
Python, Scikit-learn
SHAP / Fairlearn (optional integrations)
Jupyter Notebooks or Google Colab
Metric visualization with Matplotlib or Plotly
Target Audience:
Data scientists and AI engineers
ML professionals working in regulated industries
Product managers needing domain-specific scoring
Ethical AI researchers and evaluation specialists
Global Learning Benefits:
Build AI systems evaluated on what actually matters
Avoid misleading performance assessments
Gain buy-in from stakeholders with transparent, relevant metrics
Design metrics that reflect fairness, risk, or strategic value
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