What are the key differences between decision trees and random forests?

What are the key differences between decision trees and random forests?

Decision trees are simple models that split data based on feature values, while random forests are ensembles of multiple decision trees that improve accuracy and reduce overfitting.

Can you explain how random forests reduce overfitting?

Random forests reduce overfitting by averaging the predictions of multiple trees, which helps to smooth out noise and variance in the data.

Are there specific scenarios where one is better than the other?

Yes, decision trees are easier to interpret and work well on smaller datasets, while random forests perform better on larger datasets with complex patterns.

What are some advantages of using random forests?

Random forests provide better accuracy, handle missing values well, and are less sensitive to outliers compared to decision trees.