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公开(公告)号：US11514361B2
公开(公告)日：2022-11-29
申请号：US16557760
申请日：2019-08-30
发明人： Theodoros Salonidis , John Eversman , Dakuo Wang , Alex Swain , Gregory Bramble , Lin Ju , Nicholas Mazzitelli , Voranouth Supadulya
IPC分类号： G06F3/0482 , G06N20/00 , G06F9/54 , G06F9/38
摘要： Embodiments for providing automated machine learning visualization. Machine learning tasks, transformers, and estimators may be received into one or more machine learning composition modules. The machine learning composition modules generate one or more machine learning models. A machine learning model pipeline is a sequence of transformers and estimators and an ensemble of machine learning pipelines are an ensemble of machine learning pipelines. A machine learning model pipeline, an ensemble of a plurality of machine learning model pipelines, or a combination thereof, along with corresponding metadata, may be generated using the machine learning composition modules. Metadata may be extracted from the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof. An interactive visualization graphical user interface of the machine learning model pipeline, the ensemble of a plurality of machine learning model pipelines, or combination thereof, and the extracted metadata may be generated.
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公开(公告)号：US20220147862A1
公开(公告)日：2022-05-12
申请号：US17093978
申请日：2020-11-10
IPC分类号： G06N20/00
摘要： In an approach to creating explanatory confusion matrices, responsive to receiving a machine learning model for analysis, a confusion matrix is calculated for the machine learning model, where each cell in the confusion matrix has a corresponding set of data. A link is created from each cell in the confusion matrix to the corresponding set of data. Responsive to a user selecting in a user interface a specific cell of the confusion matrix, the corresponding set of data to the specific cell is displayed on the user interface.
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