What is overfitting and how can I prevent it?

What is overfitting and how can I prevent it?

Overfitting occurs when a model learns the training data too well, capturing noise instead of the underlying pattern. To prevent it, you can use techniques like cross-validation, regularization, and pruning.

Can you explain regularization and how it helps?

Regularization adds a penalty to the loss function to discourage overly complex models. This helps in reducing overfitting by keeping the model simpler.

What are some common regularization techniques?

Common techniques include L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net, which combines both.

How does cross-validation work?

Cross-validation involves splitting the dataset into multiple subsets, training the model on some and validating it on others. This helps ensure the model generalizes well to unseen data.

Are there any specific algorithms that are more prone to overfitting?

Yes, complex models like deep neural networks and decision trees are more prone to overfitting, especially with limited data.