What are the differences between random forests and gradient boosting?

What are the differences between random forests and gradient boosting?

Random forests use multiple decision trees to make predictions, while gradient boosting builds trees sequentially to correct errors.

Which one is better for accuracy?

Gradient boosting often provides better accuracy due to its sequential learning approach, but it can be more prone to overfitting.

Are there specific scenarios where one is preferred over the other?

Yes, random forests are great for handling large datasets with many features, while gradient boosting excels in smaller datasets with complex patterns.

What about their computational efficiency?

Random forests are generally faster to train since they can be parallelized, while gradient boosting can be slower due to its sequential nature.