What are the differences between decision trees and decision forests?

What are the differences between decision trees and decision forests?

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

Why would I choose a decision forest over a decision tree?

Decision forests generally provide better accuracy and robustness by averaging the predictions of multiple trees, reducing overfitting.

Can you explain how decision forests improve accuracy?

Sure! Decision forests use techniques like bagging and random feature selection, which help capture diverse patterns in the data.

Are there any downsides to using decision forests?

Yes, decision forests can be more computationally intensive and less interpretable than single decision trees.

What are some common applications for decision forests?

They are widely used in classification and regression tasks, such as in finance for credit scoring or in healthcare for disease prediction.