What are some techniques to reduce overfitting in machine learning?

What are some techniques to reduce overfitting in machine learning?

There are several techniques to reduce overfitting, such as cross-validation, regularization, and pruning.

Can you explain how regularization works?

Regularization adds a penalty to the loss function to discourage complex models, helping to generalize better.

What is cross-validation and how does it help?

Cross-validation involves splitting the data into subsets to train and validate the model multiple times, ensuring it performs well on unseen data.

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

Yes, algorithms like decision trees with pruning, and ensemble methods like random forests are generally less prone to overfitting.

How can I tell if my model is overfitting?

You can check for overfitting by comparing the performance metrics on training and validation datasets; a large gap indicates overfitting.