What is decision tree interpretability?

What is decision tree interpretability?

Decision tree interpretability refers to the ability to understand and explain how a decision tree model makes predictions.

Why is interpretability important in machine learning?

Interpretability is crucial because it helps users trust the model's decisions, ensures compliance with regulations, and aids in debugging.

Can you give me an example of how a decision tree makes a decision?

Sure! For instance, a decision tree might classify whether a loan should be approved based on features like credit score, income, and debt-to-income ratio.

What are the limitations of decision tree interpretability?

Some limitations include overfitting, sensitivity to data changes, and the potential for complex trees that are hard to interpret.