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Underfitting

AI/ML Fundamentals

Model is too simple to capture patterns in data

What is Underfitting?

When a model is too simple to learn underlying patterns. It performs poorly on both training and new data.

Real-World Examples

  • •Using linear regression for non-linear data
  • •Too few neurons in neural network

Common Mistakes to Avoid

Using models that are too simple for the problem

Related Terms

Learn more about concepts related to Underfitting

Overfitting

Model learns training data too well, performs poorly on new data

Bias-Variance Tradeoff

Balance between underfitting and overfitting