Literature v3 · Research topic
Which AI explainer gives more consistent answers: LIME or SHAP?
Train a model to predict income, then run LIME and SHAP multiple times to see which one gives the same top features every time.
Why this matters
Imagine you train a model to predict income, but the explanation changes every time you run it. Which explainability method should you trust? This project pits LIME against SHAP in a reproducibility showdown on a classic census dataset.
Project scores
Difficulty
This project is designed for high school students with some prior experience in Python and basic machine learning concepts. Over 8 weeks, you will learn to use LIME and SHAP libraries, run repeated experiments on a public dataset, and analyze variability in feature importance rankings. The workload is moderate, with weekly coding and analysis tasks. Prerequisites include familiarity with pandas, s
3 of 5 difficulty
Strengths
- Clear comparison of two popular explainability methods
- Reproducibility focus addresses a real research gap
- Uses a well-known benchmark dataset (UCI Adult)
- Quantitative analysis of stability across runs
Skills built
Zero-cost data
Zero-cost dataResearch gap
Imagine you train a model to predict income, but the explanation changes every time you run it. Which explainability method should you trust? This project pits LIME against SHAP in a reproducibility showdown on a classic census dataset.
Curriculum alignment
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