What are the differences between random forests and linear regression?

What are the differences between random forests and linear regression?

Random forests are ensemble methods that use multiple decision trees to improve accuracy, while linear regression models the relationship between variables with a linear equation.

When should I use random forests instead of linear regression?

Use random forests when you have complex data with non-linear relationships or interactions, while linear regression is best for simpler, linear relationships.

Can you explain how random forests handle overfitting?

Random forests reduce overfitting by averaging the predictions of multiple trees, which helps to generalize better on unseen data.

What are the advantages of using linear regression?

Linear regression is easy to interpret, requires less computational power, and works well when the relationship between variables is linear.

Are there any limitations to using random forests?

Yes, random forests can be less interpretable than linear regression and may require more computational resources.