What are the differences between decision trees and random forests?

What are the 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.

What are some advantages of using random forests over decision trees?

Some advantages include better accuracy, robustness to noise, and the ability to handle large datasets with higher dimensionality.

Are there any disadvantages to using random forests?

Yes, random forests can be more complex and less interpretable than single decision trees, and they may require more computational resources.