International Business Machines Corporation

Alex Swain

Arron J Harden

Julian J Vizor

Theodoros Salonidis

Dakuo Wang

Gregory Bramble

Stefan A G Van Der Stockt

Lin Ju

Edward J Biddle

John Dillon Eversman

Voranouth Supadulya

Michael W Yaklin

Yasmin Youssef

Nicholas Mazzitelli

Daniel Kuehn

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.

Explanatory confusion matrices for machine learning

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.

Automated artificial intelligence radial visualization

Provided are a computer implemented method, computer program product, and system for presenting information about multiple data series. The multiple data series are obtained. A main graphing area with a graph showing one or more of the obtained data series, an active process execution metrics area with information about the one or more obtained data series shown in the graph, and an available process execution metrics area with one or more thumbnail views of one or more of the obtained data series available for selection to be shown in the graph are displayed.

Simplified graphical analysis of multiple data series

Provided are techniques for viewing objects. A hierarchical structure is displayed, wherein the hierarchical structure includes two or more levels, and wherein each of the levels includes one or more objects. A control is provided to enable selection of one or more objects at each of the levels of the hierarchical structure. Selection of at least one object at each of two or more of the levels of the hierarchical structure is received. In response to receiving the selection, the hierarchical structure is displayed with the at least one object at each of the two or more of the levels shown as selected along with a list of the selected objects.

Selection and deselection of objects at multiple levels of a hierarchy

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Austin, TX, United States of America

Alex Swain