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Adaptable Interpretable Machine Learning-Recursive Bayesian Rule Lists: FY17 Line-Supported Information, Computation, and Exploitation Program

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Technical Report

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MIT Lincoln Laboratory Lexington United States

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Three important factors that influence user trust in automation and machine learning MLalgorithm are good performance, interpretability-the ability for users to understand how the automation reaches its recommendation-and adaptability-the ability for the automation to learn from new data and user feedback. This report describes work conducted under the Adaptable Interpretable Machine Learning AIM program, whose goal is to create ML algorithms that users can understand and that keep learning so users will trust them and actually use them.This report derives Recursive Bayesian Rule Lists RBRL, a group of supervised-classification algorithms that are both interpretable and adaptable. RBRL is based on Bayesian Rule Lists BRL, which are interpretable decision-list classiers that perform competitively with state-of-the art, non-interpretable classifiers on many problems, and on an analogy between classifier adaptation and recursive Bayesian tracking RBT.

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