When should I use a Random Forest model?

When should I use a Random Forest model?

You should consider using a Random Forest model when you have a large dataset with many features, especially if they are not linearly separable.

What are the advantages of using Random Forest over other models?

Random Forest is robust to overfitting, handles missing values well, and provides feature importance, making it easier to interpret.

Are there any specific scenarios where Random Forest performs poorly?

Yes, Random Forest may struggle with very high-dimensional data or when the data has a lot of noise, as it can lead to less accurate predictions.

How does Random Forest handle categorical variables?

Random Forest can handle categorical variables by converting them into numerical format using techniques like one-hot encoding.