When should I use Random Forests for my data analysis?

When should I use Random Forests for my data analysis?

Random Forests are great for handling large datasets with many features. They work well for both classification and regression tasks.

What are the advantages of using Random Forests over other algorithms?

Some advantages include their ability to handle missing values, reduce overfitting, and provide feature importance scores.

Are there any specific scenarios where Random Forests perform poorly?

Yes, they may struggle with very high-dimensional data or when the relationship between features is highly linear.

How do I choose the number of trees in my Random Forest model?

A common approach is to start with 100 trees and then use cross-validation to find the optimal number for your specific dataset.