How do random forests work?

Can you explain how random forests work in machine learning?

Sure! Random forests are an ensemble learning method that combines multiple decision trees to improve accuracy.

What are the advantages of using random forests over a single decision tree?

Random forests reduce overfitting, improve accuracy, and provide better generalization on unseen data.

How do random forests handle missing values?

They can handle missing values by using surrogate splits or by imputing missing data based on other trees.

Can you give me an example of a real-world application of random forests?

Absolutely! Random forests are often used in finance for credit scoring and in healthcare for disease prediction.