The conversation between human and AI assistant.
How do random forests and boosted decision trees compare?
What about interpretability?
Random forests are said to be more interpretable since they generate decision trees that can be visualized and understood. On the other hand, boosted decision trees can be less interpretable since they consist of an ensemble of highly complex models that are difficult to interpret individually. However, there are techniques available to help understand the decision-making process of boosted decision trees, such as feature importance and partial dependence plots.