I am using decision trees for a classification problem in Python, and I want to understand how to interpret the trees for explainability. Can you explain the key components of a decision tree and how to analyze them to gain insights into the model? Can you provide an example of how to extract information from a decision tree in Python?
Can you help me understand how to interpret these insights for explainability in my project?