What is underfitting and how detect it?
Underfitting is when your model learns too little.
How It Works:
Underfitting occurs when a model is too simple to capture data patterns, indicated by both training and validation performance being low.
Key Benefits of Avoidance:
- Ensures models learn meaningful relationships
- Delivers acceptable baseline performance
- Provides a starting point before fine-tuning
Real-World Use Cases:
- Linear regression on nonlinear trends
- Small networks on complex image tasks
FAQs
How fix underfitting?
Can more data help?
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