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

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

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

Can you explain how they improve accuracy?

They improve accuracy by averaging the predictions of multiple decision trees, which reduces variance and helps to generalize better.

Are they good for feature selection?

Yes, random forests can provide insights into feature importance, helping to identify the most significant variables in your dataset.

What about their computational efficiency?

While they can be computationally intensive, they are often faster than other ensemble methods, especially with parallel processing.

Can random forests be used for both classification and regression?

Absolutely! Random forests can be applied to both classification and regression tasks, making them versatile.