Interpretable AI Feature Importance Expert
Budget: $30 – $250 USD
I need a partner who can walk me through the full process of quantifying and explaining feature importance across several classic models—specifically Linear Regression, Decision Trees and Support Vector Machines—using Python. The goal is to compare and contrast interpretability techniques such as SHAP, LIME, PDP and ICE, then package the findings so that non-technical stakeholders can easily understand why each feature matters.
What I expect from you
• Well-structured, reproducible Python code (preferably in Jupyter notebooks) showing how each model is trained and how the above interpretability methods are applied.
• Clear visualisations and narratives that highlight where and why the different methods agree or diverge.
• At least one live session (Zoom, Meet, or similar) to walk me through your approach; the project involves explanations that are too involved for chat alone.
• A concise technical memo or slide deck summarising conclusions and recommended next steps.
Acceptance criteria
– Code runs end-to-end on a fresh environment with standard libraries (scikit-learn, pandas, numpy, matplotlib/plotly, shap, lime, etc.).
– Plots and tables directly link back to the underlying calculations, allowing me to reproduce every number.
– During our live review you can clearly justify each methodological choice and articulate trade-offs between SHAP, LIME, PDP and ICE for the three target models.
This assignment is scoped as a fixed-price engagement in the USD 150–250 range and will be the first of several projects on interpretable AI methods. If you enjoy deep dives, can communicate concepts beyond code snippets, and want recurring work, let’s get started.
What I expect from you
• Well-structured, reproducible Python code (preferably in Jupyter notebooks) showing how each model is trained and how the above interpretability methods are applied.
• Clear visualisations and narratives that highlight where and why the different methods agree or diverge.
• At least one live session (Zoom, Meet, or similar) to walk me through your approach; the project involves explanations that are too involved for chat alone.
• A concise technical memo or slide deck summarising conclusions and recommended next steps.
Acceptance criteria
– Code runs end-to-end on a fresh environment with standard libraries (scikit-learn, pandas, numpy, matplotlib/plotly, shap, lime, etc.).
– Plots and tables directly link back to the underlying calculations, allowing me to reproduce every number.
– During our live review you can clearly justify each methodological choice and articulate trade-offs between SHAP, LIME, PDP and ICE for the three target models.
This assignment is scoped as a fixed-price engagement in the USD 150–250 range and will be the first of several projects on interpretable AI methods. If you enjoy deep dives, can communicate concepts beyond code snippets, and want recurring work, let’s get started.