What are the benefits of using decision trees in data analysis?

What are the benefits of using decision trees in data analysis?

Decision trees are easy to interpret, handle both numerical and categorical data, and can model complex relationships.

Can you explain how they handle missing values?

Yes, decision trees can handle missing values by using surrogate splits, allowing them to make predictions even with incomplete data.

Are they suitable for large datasets?

Absolutely! Decision trees can efficiently process large datasets, but they may require pruning to avoid overfitting.

What are some common applications of decision trees?

Common applications include credit scoring, medical diagnosis, and customer segmentation.