The SHAP framework unifies the methods used to interpret and explain machine learning models. This post helps interpret and explain SHAP. Read this post to start getting into SHAP (with both high-level explanation and python example).
Data sets contain noise but with high-powered or elegant data science, the relevant signal can be extracted. One key technique for analysis of real-world data (primarily focused on forecasting) is time-series analysis. A popular time-series forecasting procedure is Facebook's open-source Prophet procedure. Prophet is implemented in both R and Python.
Carnegie Mellon University's Heinz College offered a unique opportunity to jointly study public policy, management, and data analytics. The coursework covers topics ranging from machine learning, deep learning, econometrics, and optimization to organizational design and decision-making. Continue reading to learn why I am glad I enrolled.
While seemingly a trivial task, classifying recipes into cuisines and understanding how to interpret clustering and classification results can help you creatively answer other questions. Continue reading to learn how.