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 improve generalization.

What are some advantages of using random forests?

Some advantages include better accuracy, robustness to noise, and the ability to handle large datasets with higher dimensionality.

Are there any disadvantages to using random forests?

Yes, random forests can be computationally intensive and may require more memory compared to single decision trees.

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

You might choose a decision tree for its simplicity and interpretability, especially for smaller datasets or when model transparency is crucial.