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- W3216394476 abstract "In this paper, we explore the intersection of knowledge and the forecasting accuracy of humans when supported by visual analytics. We have recruited 40 experts in machine learning and trained them in the use of a box office forecasting visual analytics system. Our goal was to explore the impact of visual analytics and knowledge in human-machine forecasting. This paper reports on how participants explore and reason with data and develop a forecast when provided with a predictive model of middling performance (R <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> ≈ .7). We vary the knowledge base of the participants through training, compare the forecasts to the baseline model, and discuss performance in the context of previous work on algorithmic aversion and trust." @default.
- W3216394476 created "2021-12-06" @default.
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- W3216394476 date "2021-10-01" @default.
- W3216394476 modified "2023-09-30" @default.
- W3216394476 title "Evaluating Forecasting, Knowledge, and Visual Analytics" @default.
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- W3216394476 doi "https://doi.org/10.1109/trex53765.2021.00011" @default.
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