How do we address underfitting in our models?
Balancing fit and complexity is the heart of modeling.
How It Works:
Add layers or units, switch to a more expressive architecture, reduce regularization, or engineer better features to give the model capacity to learn.
Key Benefits:
- Improved baseline accuracy
- Better utilization of data patterns
- Sets stage for further tuning
Real-World Use Cases:
- Moving from logistic regression to decision trees
- Switching from small CNN to ResNet for vision
FAQs
How detect too much complexity?
When add features vs. increase model?
.png)
.svg)
