What are the advantages of using Random Forest in machine learning?

What are the advantages of using Random Forest in machine learning?

Random Forest is great for handling large datasets, reducing overfitting, and improving accuracy.

Can you explain how it reduces overfitting?

It reduces overfitting by averaging multiple decision trees, which helps to generalize better on unseen data.

Are there any specific scenarios where Random Forest performs better?

Yes, it excels in scenarios with high dimensionality and when there are complex interactions between features.

What are some limitations of Random Forest?

Some limitations include longer training times and less interpretability compared to simpler models.