| 📅 Today’s Picks |
Yellowbrick: Detect Overfitting vs Underfitting Visually
Problem:
Hyperparameter tuning requires finding the sweet spot between underfitting (model too simple) and overfitting (model memorizes training data).
You could write the loop, run cross-validation for each value, collect scores, and format the plot yourself. But that’s boilerplate you’ll repeat across projects.
Solution:
Yellowbrick is a machine learning visualization library built for exactly this.
Its ValidationCurve shows you what’s working, what’s not, and what to fix next without the boilerplate or inconsistent formatting.
How to read the plot in this example:
- Training score (blue) stays high as max_depth increases
- Validation score (green) drops after depth 4
- The growing gap means the model memorizes training data but fails on new data
Action: Pick max_depth around 3-4 where validation score peaks before the gap widens.
Full Article:
PydanticAI: Type-Safe LLM Outputs with Auto-Validation
Problem:
Without structured outputs, you’re working with raw text that might not match your expected format.
Unexpected responses, missing fields, or wrong data types can cause errors that are easy to miss during development.
Solution:
PydanticAI uses Pydantic models to automatically validate and structure LLM responses.
Key benefits:
- Type safety at runtime with validated Python objects
- Automatic retry on validation failures
- Direct field access without manual parsing
- Integration with existing Pydantic workflows
LangChain works too, but PydanticAI is a lighter alternative when you just need structured outputs.
Full Article:
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