A consultant experienced in developing predictive models for his clients to improve business operations, Everett Blake, was working with the Richardson Eye Care and Surgery Center (RECSC), which wanted to explore ways to improve its no-show rate, classifying a “no-show” as an appointment not kept without 24-hours’ notice. After running variables through an XGBoost model, Blake’s company had identified predictive variables, which included age and ...

The case focuses on UW Hospital's approach to improve the accuracy of breast cancer detection using machine learning. Researchers at the University of Wisconsin assembled a unique data set comprising 569 examples of breast cancer biopsies to train a classification model (e.g., logistic regression or classification tree) for breast cancer detection.

Teaching note for product F-1985

Machine-learning (ML) models have become a common tool used across a multitude of industries to help people make decisions. As these models have increased in predictive power, many have also grown in complexity. The pursuit to improve the accuracy of predictions has diminished the interpretability of many models—leaving users with little understanding of the model’s behavior or trust in its predictions. The field of eXplainable artificial intelli...

Machine-learning (ML) models have become a common tool used across a multitude of industries to help people make decisions. As these models have increased in predictive power, many have also grown in complexity. The pursuit to improve the accuracy of predictions has diminished the interpretability of many models—leaving users with little understanding of the model’s behavior or trust in its predictions. The field of eXplainable artificial intelli...