What are the applications of random forests in machine learning?

What are the applications of random forests in machine learning?

Random forests are used for classification, regression, and feature selection. They excel in handling large datasets.

Can you give me an example of how they are used in real-world scenarios?

Sure! They're often used in finance for credit scoring and in healthcare for disease prediction.

What are the advantages of using random forests over other algorithms?

They reduce overfitting, handle missing values well, and provide feature importance, making them very robust.

Are there any limitations to using random forests?

Yes, they can be less interpretable than simpler models and may require more computational resources.