How can I handle overfitting in machine learning?

How can I handle overfitting in machine learning?

Overfitting occurs when your model learns the training data too well, including noise. You can handle it by using techniques like cross-validation, regularization, or pruning.

What is cross-validation and how does it help?

Cross-validation is a technique to assess how the results of a statistical analysis will generalize to an independent dataset. It helps in ensuring that your model is not overfitting by validating it on different subsets of the data.

Can you explain regularization?

Regularization adds a penalty to the loss function to discourage complex models. Techniques like L1 and L2 regularization help in reducing overfitting by keeping the model simpler.

What is pruning in decision trees?

Pruning is the process of removing sections of a decision tree that provide little power in predicting target variables. This helps in reducing overfitting by simplifying the model.