Master Study AI

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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