Unveiling the Truth Behind Black-Box Models: Why They May Not Always Be the Most Accurate Choice
Welcome to the world of data science, where the allure of cutting-edge models like XGBoost and Neural Networks can be tempting. However, these sophisticated black-box models often present a significant challenge — the ability to explain their decisions to others.
Who would’ve guessed that simply understanding the output of automated systems would be such a critical component?
But fear not, there is a way to have it all. Enter the realm of model agnostic methods, where you can harness the power of black box models while retaining the ability to explain them using techniques like SHAP, LIME, PDPs, ALEs, and Friedman’s H-stat. The best of both worlds is within reach — accuracy and interpretability combined!
Or is it?
While the promise of top-notch performance is alluring, it’s important not to lose sight of the ultimate goal of machine learning: making accurate predictions on unseen data. Let’s delve into why relying solely on complex models may not always be the most effective way to achieve this, even with the ability to explain them using supplementary methods.