Capstone Project: Error Report and Model Diagnostic
artificial-intelligence-ai.
Course Modules:
Module 1: Define the Scope of Error Analysis
Choose a domain (e.g., sentiment analysis, fraud detection, medical diagnosis)
Set business priorities: accuracy, safety, fairness, or cost impact
Gather a dataset with clear ground truth and model predictions
Module 2: Identify and Categorize Errors
Generate confusion matrix (TP, FP, TN, FN)
Explore class imbalance, boundary errors, and edge cases
Segment errors by user group, feature, or class type
Module 3: Statistical & Visual Diagnostics
Analyze error distribution using charts and histograms
Compare false positives vs. false negatives
Use SHAP/LIME for interpretability on misclassified instances
Module 4: Root Cause Analysis
Investigate data quality issues (e.g., mislabeled data, missing features)
Review model assumptions and overfitting/underfitting
Test sensitivity to hyperparameters or training data shifts
Module 5: Reporting and Recommendation
Write a structured error report covering:
Key metrics (accuracy, recall, AUC, etc.)
Error trends and critical weaknesses
Suggested improvements (relabeling, rebalancing, new features)
Deliverables:
Jupyter Notebook or Google Colab walkthrough
PDF or slide-based executive summary
GitHub link with reproducible code and charts
Tools & Technologies Used:
Python (Pandas, Scikit-learn, Matplotlib, Seaborn)
SHAP / LIME (optional for explainability)
Jupyter Notebook or Google Colab
Excel or Google Sheets (optional for tabular summaries)
Target Audience:
Data scientists and AI learners completing core model development
QA teams evaluating AI systems before deployment
Students preparing for roles in AI testing or MLOps
Engineers focusing on responsible AI diagnostics
Global Learning Benefits:
Gain expertise in AI model error diagnosis
Communicate findings clearly across technical and business teams
Improve AI system performance through focused evaluation
Build a portfolio-ready report showcasing critical thinking in ML
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