What are the differences between random forests and decision trees?

What are the differences between random forests and decision trees?

Decision trees are simple models that split data based on feature values, while random forests combine multiple decision trees to improve accuracy and reduce overfitting.

Can you explain how random forests reduce overfitting?

Random forests reduce overfitting by averaging the predictions of multiple decision trees, which helps to smooth out noise and variance in the data.

What are the advantages of using random forests over decision trees?

Random forests generally provide better accuracy, robustness to noise, and handle large datasets with higher dimensionality better than single decision trees.

Are there any disadvantages to using random forests?

Yes, random forests can be more complex and less interpretable than decision trees, and they may require more computational resources.

In what scenarios would you recommend using decision trees instead?

I would recommend decision trees for simpler problems where interpretability is crucial, or when computational resources are limited.