What are the limitations of Random Forest algorithms?

What are the limitations of Random Forest algorithms?

Random Forest algorithms have several limitations, including overfitting on noisy data, difficulty in interpreting results, and high computational cost.

Can you explain how overfitting occurs in Random Forest?

Overfitting in Random Forest can occur when the model becomes too complex, capturing noise in the training data rather than the underlying pattern.

What can be done to mitigate overfitting in Random Forest?

To mitigate overfitting, you can limit the depth of the trees, reduce the number of trees, or use techniques like cross-validation.

Are there any specific scenarios where Random Forest is not recommended?

Yes, Random Forest is not recommended for very high-dimensional data with few samples, as it may struggle to generalize well.