What are the advantages of decision trees over other machine learning tools?

What are the advantages of decision trees over other machine learning tools?

Decision trees are easy to interpret and visualize, making them user-friendly. They handle both numerical and categorical data well.

Can you explain how decision trees handle overfitting?

Yes, decision trees can overfit the training data. Techniques like pruning, setting a maximum depth, or using ensemble methods can help mitigate this.

What are some common use cases for decision trees?

Common use cases include classification tasks in finance, healthcare, and marketing, as well as regression problems.

How do decision trees compare to random forests?

Random forests are an ensemble of decision trees, which improves accuracy and reduces overfitting by averaging multiple trees.

Are there any disadvantages to using decision trees?

Yes, they can be unstable with small changes in data and may not perform well on complex datasets compared to other algorithms.