What is overfitting and underfitting?

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Overfitting and Underfitting are common issues in machine learning that affect model performance.

1. Overfitting

• Definition: The model learns the training data too well, including noise and random fluctuations, making it perform poorly on new data.

• Symptoms:

  • Very high accuracy on training data but low accuracy on test data.

• Causes:

  • Too complex model (many parameters).

  • Insufficient training data.

• Solutions:

  • Simplify the model.

  • Use regularization (L1/L2).

  • Get more data or use dropout in neural networks.

2. Underfitting

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

• Symptoms:

  • Low accuracy on both training and test data.

• Causes:

  • Model is not complex enough.

  • Too few training epochs or wrong features.

• Solutions:

  • Use a more complex model.

  • Add more relevant features.

  • Train longer.

Summary:

• Overfitting: Model memorizes data → poor generalization.

• Underfitting: Model fails to learn patterns → poor performance overall.

Read More :

What is the difference between supervised and unsupervised learning?

How do you handle imbalanced datasets?

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