What are the differences between Random Forest and Decision Tree?

What are the differences between Random Forest and Decision Tree?

A Decision Tree is a single tree structure that makes decisions based on feature splits, while Random Forest is an ensemble of multiple Decision Trees that improves accuracy and reduces overfitting.

Can you explain how Random Forest reduces overfitting?

Random Forest reduces overfitting by averaging the results of multiple trees, which helps to smooth out the noise and variance in the data.

Which one is better for large datasets?

Random Forest is generally better for large datasets as it can handle high dimensionality and provides better generalization.

Are there any scenarios where a Decision Tree might be preferred?

Yes, Decision Trees are preferred for their simplicity and interpretability, especially in cases where model transparency is crucial.