What are the limitations of Random Forests in machine learning?

What are the limitations of Random Forests in machine learning?

Random Forests, while powerful, have limitations such as overfitting, interpretability issues, and high computational cost.

Can you explain how overfitting occurs in Random Forests?

Overfitting in Random Forests can occur when the model becomes too complex, capturing noise in the training data instead of the underlying pattern.

What about interpretability? Why is it a concern?

Interpretability is a concern because Random Forests create many decision trees, making it difficult to understand how decisions are made.

Are there any scenarios where Random Forests perform poorly?

Yes, Random Forests may perform poorly on datasets with high dimensionality and sparse features, as they can struggle to find meaningful splits.

What alternatives should I consider if Random Forests aren't suitable?

If Random Forests aren't suitable, consider alternatives like Gradient Boosting Machines, Support Vector Machines, or Neural Networks.