Abstract: A computer device receives user selection of a plurality of data fields from a data set. The computer device generates a plurality of data visualization options that use a majority of the plurality of data fields. The computer device computes, for each data visualization option of the plurality of data visualization options, a respective score for the respective data visualization option according to a set of ranking criteria. The set of ranking criteria includes a first ranking criterion that is based on values of one or more of the user-selected data fields in the data set. The computer device creates a ranked list of the data visualization options. The ranked list is ordered according to a plurality of computed scores corresponding to the plurality of data visualization options. The computer device presents the ranked list to the user.

Abstract: The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

Abstract: A method constructs data visualization options. A user selects data fields from a data set. The process identifies a first plurality of data visualizations that use all of the user-selected data fields. For each data visualization, the process computes a score, based in part on visual patterns corresponding to statistical properties of data values of a user-selected data field. The process also identifies a second plurality of data visualizations, each using a majority of the user-selected data fields and also using a respective additional data field that is not one of the user-selected data fields. For each of the second plurality of data visualizations, the process computes a respective score, which uses data values of the respective additional data field. The process forms a recommended set of data visualizations including high-scoring data visualizations from both the first and second pluralities. The process presents the recommended data visualizations to the user.

Abstract: The subject technology receives information associated with a machine learning model. The subject technology determines a set of metrics based at least in part on the information associated with the machine learning model, where the set of metrics corresponds to respective indicators of performance of the machine learning model based on input data from a data set, the set of metrics further including a number of errors produced by the machine learning model when applied to the input data from the data set. Further, the subject technology displays a user interface based at least in part on the set of metrics, where the user interface includes a set of graphical elements, and the set of graphical elements further includes representations of the set of metrics, and representations of the input data from the data set utilized by the machine learning model.

Abstract: A method constructs data visualization options. A user selects data fields from a data set. The process identifies a first plurality of data visualizations that use all of the user-selected data fields. For each data visualization, the process computes a score, based in part on visual patterns corresponding to statistical properties of data values of a user-selected data field. The process also identifies a second plurality of data visualizations, each using a majority of the user-selected data fields and also using a respective additional data field that is not one of the user-selected data fields. For each of the second plurality of data visualizations, the process computes a respective score, which uses data values of the respective additional data field. The process forms a recommended set of data visualizations including high-scoring data visualizations from both the first and second pluralities. The process presents the recommended data visualizations to the user.

Abstract: A method of ranking data visualizations is performed at a computing device having one or more processors and memory. The memory stores one or more programs for execution by the one or more processors. A user selects a set of data fields from a set of data. The computing device identifies a plurality of data visualizations based on the data fields selected by the user. For each of the plurality of data visualizations, a score is computed based on a set of ranking criteria. A first ranking criterion of the set of ranking criteria is based on values of one or more of the user-selected data fields in the set of data. A ranked list of the data visualizations is created, which is ordered according to the computed scores of the data visualizations. The ranked list is presented to the user.

Abstract: A method of ranking data visualizations is performed at a computing device having one or more processors and memory. The memory stores one or more programs for execution by the processors. A user selects a plurality of data fields from a set of data, and the computing device generates a plurality of data visualization options. Each data visualization option associates each of the user-selected data fields with a respective predefined visual specification feature. For each of the generated data visualization options, the device calculates a score based on a set of ranking criteria. A first ranking criterion of the set of ranking criteria is based on values of one or more of the user-selected data fields in the set of data. A ranked list of the data visualization options is then created, where the ranked list is ordered according to the computed scores of the data visualization options.

Abstract: A method ranks data visualizations. A user selects a set of data fields from a set of data. A device identifies a plurality of data visualizations that use each data field in the user-selected set of data fields and identifies a plurality of respective alternative data visualizations. Each respective alternative data visualization uses each data field in a respective modified set of data fields. Each respective modified set differs from the user-selected set by a limited sequence of atomic operations. For each of the data visualizations and each of the alternative data visualizations, the device computes a score based on a set of ranking criteria. At least one criterion used to compute each score uses values of one or more of the data fields in the set of data. The list of high scoring data visualizations and alternative data visualizations is presented to the user.

Abstract: A method of ranking data visualizations is performed at a computing device having one or more processors and memory. The memory stores one or more programs for execution by the one or more processors. A user selects a set of data fields from a set of data. The computing device identifies a plurality of data visualizations based on the data fields selected by the user. For each of the plurality of data visualizations, a score is computed based on a set of ranking criteria. A first ranking criterion of the set of ranking criteria is based on values of one or more of the user-selected data fields in the set of data. A ranked list of the data visualizations is created, which is ordered according to the computed scores of the data visualizations. The ranked list is presented to the user.

Abstract: A method of ranking data visualizations is performed at a computing device having one or more processors and memory. The memory stores one or more programs for execution by the processors. A user selects a plurality of data fields from a set of data, and the computing device generates a plurality of data visualization options. Each data visualization option associates each of the user-selected data fields with a respective predefined visual specification feature. For each of the generated data visualization options, the device calculates a score based on a set of ranking criteria. A first ranking criterion of the set of ranking criteria is based on values of one or more of the user-selected data fields in the set of data. A ranked list of the data visualization options is then created, where the ranked list is ordered according to the computed scores of the data visualization options.