Explain overfitting and underfitting ? Techniques to avoid this

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Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, while underfitting occurs when a model is too simple to capture the underlying pattern.

Overfitting: Model performs well on training data but poorly on unseen data. Can be avoided by using techniques like cross-validation, regularization, and early stopping.
Underfitting: Model is too simple to capture the underlying pattern. Can be avoided by using more complex models, increasing the model's capacity, or adding more features.
Examples: Overfitting - Decision trees with too many branches. Underfitting - Linear regression on non-linear data.
Regularization techniques like L1 and L2 regularization can help prevent overfitting by penalizing large coefficients in the model.

Explain overfitting and underfitting ? Techniques to avoid this

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