Model Interpretability in Machine Learning Interviews: Key Questions
Model interpretability has become a central topic in data science interviews, especially as businesses transition from traditional models like logistic regression to powerful machine learning algorithms such as XGBoost. While advanced models can improve predictive accuracy, stakeholders—particularly in regulated sectors like finance—demand clear explanations for model decisions. In this article, we’ll address the popular machine learning interview question:
“The client has given you permission to work on an XGBoost (Loan Approver) model instead of a traditional logistic regression model. But he is very keen on interpretability. How will you make sure your model is explaining the importance of features?”
This comprehensive guide will walk you through the main interpretability tools for XGBoost: Feature Importance, SHAP Values, and Partial Dependence Plots (PDPs). We’ll explain each concept, provide practical code examples, and discuss best practices for communicating model insights to stakeholders.
