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Interpretable classifiers using rules and Bayesian analysis: Building a better stroke prediction model
Benjamin Letham, Cynthia Rudin, Tyler H. McCormick +1
We aim to produce predictive models that are not only accurate, but are also interpretable to human experts. Our models are decision lists, which consist of a series of if...then..…
Prediction uncertainty and optimal experimental design for learning dynamical systems
Benjamin Letham, Portia A. Letham, Cynthia Rudin +1
Dynamical systems are frequently used to model biological systems. When these models are fit to data it is necessary to ascertain the uncertainty in the model fit. Here we present…
Reactive point processes: A new approach to predicting power failures in underground electrical systems
Şeyda Ertekin, Cynthia Rudin, Tyler H. McCormick
Reactive point processes (RPPs) are a new statistical model designed for predicting discrete events in time based on past history. RPPs were developed to handle an important proble…
Bayesian Inference of Arrival Rate and Substitution Behavior from Sales Transaction Data with Stockouts
Benjamin Letham, Lydia M. Letham, Cynthia Rudin
When an item goes out of stock, sales transaction data no longer reflect the original customer demand, since some customers leave with no purchase while others substitute alternati…