Assignment #2: ML Pipelines, Feature Engineering, and Hyperparameter Tuning#3
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Assignment #2: ML Pipelines, Feature Engineering, and Hyperparameter Tuning#3
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fyellow
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Jun 23, 2025
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Excellent job! You’ve correctly implemented and tuned four pipelines but shap.explainers.Linear isn't compatible with KNN, which caused the error. To fix this, use shap.Explainer(best_overall_model.named_steps["regressor"], transformed_data) instead or switch to Ridge if you want SHAP coefficients.
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Good Job! Approved |
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Uoft-DSI | Production Data Engineering - Assignment #2
What changes are you trying to make? (e.g. Adding or removing code, refactoring existing code, adding reports)
What did you learn from the changes you have made?
Was there another approach you were thinking about making? If so, what approach(es) were you thinking of?
Were there any challenges? If so, what issue(s) did you face? How did you overcome it?
How were these changes tested?
A reference to a related issue in your repository (if applicable)
Checklist