Underfitting
When a model is too simple to capture underlying patterns in data, performing poorly on both training and test data.
In more detail
Underfitting happens when model capacity is insufficient or training is inadequate. The model has high bias and cannot learn complex patterns. Solutions include increasing model complexity, training longer, using more features, or reducing regularization.
Examples
- Using linear regression for non-linear data
- Training too briefly
- Model too small for task complexity
Related terms
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