What are the advantages of decision trees?
An example of an AI chatbot conversation about the benefits of decision trees in data analysis.
What are the advantages of using decision trees in machine learning?
Decision trees are easy to understand and interpret, handle both numerical and categorical data, and require little data preprocessing.
Can you explain how they handle missing values?
Yes, decision trees can handle missing values by using surrogate splits, which allow them to make decisions even when some data is missing.
Are they prone to overfitting?
Yes, decision trees can overfit the training data, but techniques like pruning can help mitigate this issue.
What are some real-world applications of decision trees?
They are widely used in finance for credit scoring, in healthcare for diagnosis, and in marketing for customer segmentation.
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