Model Evaluation and Interpretation

Now let’s evaluate our tuned model properly and save it for future use. We’ll use metrics beyond accuracy to get a complete picture of performance.

Using the Best Model

GridSearchCV gives us the best model already fitted. Let’s use it for final evaluation:

Classification Report

Accuracy alone doesn’t tell the whole story. The classification report shows precision, recall, and F1-score for each class:

Confusion Matrix

The confusion matrix shows exactly where the model makes mistakes:

Feature Importance (Optional)

For tree-based models, we can see which features are most important:

Saving the Pipeline

Once we’re happy with the model, we save it. The whole pipeline (preprocessing + model) gets saved together:

Loading and Using the Pipeline

Later, you can load the pipeline and use it for predictions:

Why Save the Whole Pipeline?

Saving just the model would require you to:

1. Remember which preprocessing was used
2. Manually apply preprocessing to new data
3. Keep preprocessing code in sync with the model

Saving the pipeline means:

• One file contains everything
• Preprocessing is automatic
• No chance of mismatched preprocessing
• Production-ready pattern

Prediction Playground

Try making predictions on custom values:

Final Knowledge Check

Summary

Congratulations! You’ve built a complete ML pipeline. Here’s what you learned:

✅ Data Loading - Loaded and explored the Wine dataset
✅ Baseline Model - Created a simple model for comparison
✅ Preprocessing - Used ColumnTransformer for feature scaling
✅ Pipelines - Combined preprocessing and model into a Pipeline
✅ Cross-Validation - Used cross-validation for reliable evaluation
✅ Hyperparameter Tuning - Optimized parameters with GridSearchCV
✅ Evaluation - Evaluated with classification report and confusion matrix
✅ Saving - Saved the pipeline for reuse

Next Steps

Now that you understand pipelines, you can:

• Handle missing values - Add SimpleImputer to your pipeline
• Work with real datasets - Apply this to your own data
• Build regression pipelines - Same concepts apply to regression
• Create custom transformers - Build your own preprocessing steps
• Deploy to production - Use the saved pipeline in your applications

Resources

• Scikit-Learn Pipeline Documentation
• ColumnTransformer Guide
• GridSearchCV Documentation

Thanks for completing this tutorial! You now have the skills to build production-ready ML pipelines.