Bayesian Networks vs Decision Trees

What are the main differences between Bayesian networks and decision trees?

Bayesian networks are probabilistic graphical models that represent a set of variables and their conditional dependencies. Decision trees, on the other hand, are a flowchart-like structure used for classification and regression tasks, where decisions are made based on feature values.

In which scenarios would you prefer one over the other?

You might prefer Bayesian networks when dealing with uncertainty and needing to model complex relationships between variables. Decision trees are often favored for their simplicity and interpretability, especially in cases with clear decision paths.

Can you give an example of a real-world application for each?

Sure! Bayesian networks are commonly used in medical diagnosis to model the relationships between symptoms and diseases. Decision trees are often used in customer segmentation for marketing strategies, helping businesses decide which customers to target.

Are there any limitations to using these models?

Yes, Bayesian networks can be computationally intensive and require a lot of data to accurately estimate probabilities. Decision trees can be prone to overfitting, especially with noisy data or when they are too deep.