How do decision trees differ from random forests?

How do decision trees differ from random forests?

Decision trees are single models that split data based on feature values, while random forests are ensembles of multiple decision trees.

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

Random forests reduce overfitting and improve accuracy by averaging the predictions of multiple trees, making them more robust.

Can you explain how random forests handle missing values?

Random forests can handle missing values by using surrogate splits, which allow the model to make predictions even when some data is missing.

Are there any disadvantages to using random forests?

Yes, random forests can be computationally intensive and less interpretable than single decision trees, making them harder to explain.

When should I choose a decision tree over a random forest?

Choose a decision tree for simpler problems where interpretability is key, or when computational resources are limited.