What are some effective solutions to overfitting in machine learning?

What are some effective solutions to overfitting in machine learning?

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

Can you explain regularization techniques?

Sure! Regularization techniques like L1 and L2 add a penalty to the loss function to discourage complex models.

What is cross-validation and how does it help?

Cross-validation involves splitting the dataset into training and validation sets to ensure the model generalizes well.

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

Yes, algorithms like Random Forests and Gradient Boosting are generally more robust against overfitting.