When should I use decision trees in machine learning?

When should I use decision trees in machine learning?

Decision trees are great for classification and regression tasks, especially when you need interpretability.

What are the advantages of using decision trees?

They are easy to understand, require little data preprocessing, and can handle both numerical and categorical data.

Are there any disadvantages to decision trees?

Yes, they can easily overfit the training data and may not perform well on unseen data without proper pruning.

In what scenarios are decision trees not recommended?

Avoid using them for very large datasets or when the relationships in the data are highly complex.