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      Visus: An Interactive System for Automatic Machine Learning Model Building and Curation

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          Abstract

          While the demand for machine learning (ML) applications is booming, there is a scarcity of data scientists capable of building such models. Automatic machine learning (AutoML) approaches have been proposed that help with this problem by synthesizing end-to-end ML data processing pipelines. However, these follow a best-effort approach and a user in the loop is necessary to curate and refine the derived pipelines. Since domain experts often have little or no expertise in machine learning, easy-to-use interactive interfaces that guide them throughout the model building process are necessary. In this paper, we present Visus, a system designed to support the model building process and curation of ML data processing pipelines generated by AutoML systems. We describe the framework used to ground our design choices and a usage scenario enabled by Visus. Finally, we discuss the feedback received in user testing sessions with domain experts.

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          Fairness Beyond Disparate Treatment & Disparate Impact

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            Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

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              RuleMatrix: Visualizing and Understanding Classifiers with Rules

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                Author and article information

                Journal
                05 July 2019
                Article
                10.1145/3328519.3329134
                1907.02889
                4d67cf13-d397-4493-a664-5078ded39094

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                History
                Custom metadata
                Accepted for publication in the 2019 Workshop on Human-In-the-Loop Data Analytics (HILDA'19), co-located with SIGMOD 2019
                cs.LG cs.HC

                Artificial intelligence,Human-computer-interaction
                Artificial intelligence, Human-computer-interaction

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