What are the advantages of random forests over other algorithms?

What are the advantages of random forests over other algorithms?

Random forests are robust against overfitting, handle large datasets well, and provide feature importance.

Can you explain how random forests work?

Sure! Random forests use multiple decision trees to make predictions, averaging their results for better accuracy.

What are some scenarios where I should use random forests?

They are great for classification tasks, especially when you have a mix of numerical and categorical data.

How do they compare to support vector machines?

Random forests are generally easier to tune and interpret, while SVMs can be more effective in high-dimensional spaces.

Are there any disadvantages to using random forests?

Yes, they can be slower to predict than simpler models and may require more memory due to multiple trees.