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公开(公告)号：US10984283B2
公开(公告)日：2021-04-20
申请号：US16565922
申请日：2019-09-10
Applicant: salesforce.com, inc.
Inventor： Sarah Aerni , Natalie Casey , Shubha Nabar , Melissa Runfeldt , Sara Beth Asher
Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.
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公开(公告)号：US20210073579A1
公开(公告)日：2021-03-11
申请号：US16565922
申请日：2019-09-10
Applicant: salesforce.com, inc.
Inventor： Sarah Aerni , Natalie Casey , Shubha Nabar , Melissa Runfeldt , Sara Beth Asher
Abstract: A method of training a predictive model to predict a likely field value for one or more user selected fields within an application. The method comprises providing a user interface for user selection of the one or more user selected fields within the application; analyzing a pre-existing, user provided data set of objects; training, based on the analysis, the predictive model; determining, for each user selected field based on the analysis, a confidence function for the predictive model that identifies the percentage of cases predicted correctly at different applied confidence levels, the percentage of cases predicted incorrectly at different applied confidence levels, and the percentage of cases in which the prediction model could not provide a prediction at different applied confidence levels; and providing a user interface for user review of the confidence functions for user selection of confidence threshold levels to be used with the predictive model.
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公开(公告)号：US20190138946A1
公开(公告)日：2019-05-09
申请号：US15884878
申请日：2018-01-31
Applicant: salesforce.com, inc.
Inventor： Sara Beth Asher , John Emery Ball , Vitaly Gordon , Till Christian Bergmann , Kin Fai Kan , Chalenge Masekera , Shubha Nabar , Nihar Dandekar , James Reber Lewis
Abstract: A system may automatically generate a predictive machine learning model by automatically performing various processes based on an analysis of the data as well as metadata associated with the data. The system may accept a selection of data and a prediction field from the data. The system may automatically generate a set of features based on the data and may automatically remove certain features that cause inaccuracies in the model. The system may balance the data based on a representation rate of certain outcomes. The system may train and select a model based on several candidate models. The system may then perform the predictions based on the selected model and send an indication of the predictions to a user.
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