What are the benefits of using random forests in machine learning?

What are the benefits of using random forests in machine learning?

Random forests offer several benefits, including improved accuracy, robustness to overfitting, and the ability to handle large datasets.

How do they compare to other algorithms?

Compared to algorithms like decision trees, random forests reduce variance and improve predictive performance by averaging multiple trees.

Are they easy to interpret?

While random forests are powerful, they can be less interpretable than simpler models like linear regression due to their ensemble nature.

What types of problems are they best suited for?

Random forests are great for classification and regression tasks, especially when dealing with complex datasets with many features.

Can they handle missing values?

Yes, random forests can handle missing values well, as they can use surrogate splits to make predictions even with incomplete data.