What are the advantages of Random Forest?

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

Random Forest offers several advantages, including high accuracy, robustness to overfitting, and the ability to handle large datasets.

Can you explain how it handles overfitting?

Sure! Random Forest reduces overfitting by averaging multiple decision trees, which helps to generalize better on unseen data.

What about its performance with missing values?

Random Forest can handle missing values effectively by using surrogate splits, allowing it to maintain accuracy even with incomplete data.

Are there any disadvantages I should be aware of?

Yes, some disadvantages include longer training times and less interpretability compared to simpler models like decision trees.

How does it compare to other algorithms?

Random Forest generally outperforms many algorithms in terms of accuracy and robustness, especially in complex datasets.