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Data Science Internship Prep
·
Module 7
Model Explainability and Deployment
1
Understanding Feature Importance in Tree Models
Interpret feature importance scores from tree-based models.
Explaining Models with SHAP
Use the SHAP library to explain individual predictions and overall model behavior.
3
Visualizing SHAP Values
Generate and interpret SHAP summary plots and dependence plots.
4
Diagnosing Model Failures with Error Analysis
Perform systematic error analysis to identify a model's failure modes on data subsets.
5
Error Analysis-Driven Model Improvement
Use error analysis findings to guide feature engineering and model improvements.
6
Deploy Scikit-learn Models with FastAPI
Build a simple REST API to serve a trained scikit-learn model using FastAPI.
7
Testing Model Endpoints
Test the deployed model endpoint with sample requests.
Previous module
Supervised Learning: Regression and Tree-Based Models
Next module
End-to-End Machine Learning Portfolio Project