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Fibers of Failure: Classifying Errors in Predictive Processes

KTH Royal Institute of Technology, Brinellvägen 8, 114 28 Stockholm, Sweden
Department of Mathematics, CUNY College of Staten Island, 2800 Victory Blvd, Staten Island, NY 10314, USA
Computer Science, CUNY Graduate Center, 365 5th Ave, New York, NY 10016, USA
Department of Mathematics, Stanford University, 450 Serra Mall, Stanford, CA 94305, USA
Unbox AI, Stanford, CA 94305, USA
Authors to whom correspondence should be addressed.
Algorithms 2020, 13(6), 150;
Received: 29 May 2020 / Revised: 18 June 2020 / Accepted: 20 June 2020 / Published: 23 June 2020
(This article belongs to the Special Issue Topological Data Analysis)
Predictive models are used in many different fields of science and engineering and are always prone to make faulty predictions. These faulty predictions can be more or less malignant depending on the model application. We describe fibers of failure (FiFa), a method to classify failure modes of predictive processes. Our method uses Mapper, an algorithm from topological data analysis (TDA), to build a graphical model of input data stratified by prediction errors. We demonstrate two ways to use the failure mode groupings: either to produce a correction layer that adjusts predictions by similarity to the failure modes; or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode. We demonstrate FiFa on two scenarios: a convolutional neural network (CNN) predicting MNIST images with added noise, and an artificial neural network (ANN) predicting the electrical energy consumption of an electric arc furnace (EAF). The correction layer on the CNN model improved its prediction accuracy significantly while the inspection of failure modes for the EAF model provided guiding insights into the domain-specific reasons behind several high-error regions. View Full-Text
Keywords: topological data analysis; mapper; predictive model; interpretable machine learning topological data analysis; mapper; predictive model; interpretable machine learning
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Carlsson, L.S.; Vejdemo-Johansson, M.; Carlsson, G.; Jönsson, P.G. Fibers of Failure: Classifying Errors in Predictive Processes. Algorithms 2020, 13, 150.

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