Explain the concept of overfitting and underfitting in AI models.

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Overfitting and Underfitting are common issues in AI/ML model performance:

1. Overfitting –

   • The model learns the training data too well, capturing noise and random fluctuations along with actual patterns.

   • It performs excellently on training data but poorly on unseen data due to poor generalization.

   • Causes: Too complex model, too many features, insufficient training data.

   • Solution: Use regularization, reduce complexity, increase data, apply cross-validation.

2. Underfitting –

   • The model is too simple to capture underlying patterns in data.

   • It performs poorly on both training and test data.

   • Causes: Oversimplified model, too few features, inadequate training.

   • Solution: Use a more complex model, add relevant features, train longer.

Summary:

• Overfitting → High variance, low bias.

• Underfitting → High bias, low variance.
  The goal is to find the right balance for optimal generalization.

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