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公开(公告)号：US20200250587A1
公开(公告)日：2020-08-06
申请号：US16263927
申请日：2019-01-31
Applicant: salesforce.com, inc.
Inventor： Sarah Aerni , Luke Sedney , Kin Fai Kan , Till Christian Bergmann
Abstract: The system and methods of the disclosed subject matter provide an experimentation framework to allow a user to perform machine learning experiments on tenant data within a multi-tenant database system. The system may provide an experimental interface to allow modification of machine learning algorithms, machine learning parameters, and tenant data fields. The user may be prohibited from viewing any of the tenant data or may be permitted to view only a portion of the tenant data. Upon generating an experimental model using the experimental interface, the user may view results comparing the performance of the experimental model with a current production model.
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Patent Agency Ranking
公开(公告)号：US11526799B2
公开(公告)日：2022-12-13
申请号：US16264583
申请日：2019-01-31
Applicant: salesforce.com, inc.
Inventor： Kevin Moore , Leah McGuire , Eric Wayman , Shubha Nabar , Vitaly Gordon , Sarah Aerni
Abstract: Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.
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公开(公告)号：US20200057958A1
公开(公告)日：2020-02-20
申请号：US16264583
申请日：2019-01-31
Applicant: salesforce.com, inc.
Inventor： Kevin Moore , Leah McGuire , Eric Wayman , Shubha Nabar , Vitaly Gordon , Sarah Aerni
Abstract: Methods and systems are provided to determine suitable hyperparameters for a machine learning model and/or feature engineering process. A suitable machine learning model and associated hyperparameters are determined by analyzing a dataset. Suitable hyperparameter values for compatible machine learning models having one or more hyperparameters in common and a compatible dataset schema are identified. Hyperparameters may be ranked according to each of their respective influences on a model performance metrics, and hyperparameter values identified as having greater influence may be more aggressively searched.
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公开(公告)号：US11720825B2
公开(公告)日：2023-08-08
申请号：US16263927
申请日：2019-01-31
Applicant: salesforce.com, inc.
Inventor： Sarah Aerni , Luke Sedney , Kin Fai Kan , Till Christian Bergmann
CPC classification number: G06N20/20 , G06F11/3466 , G06N5/043
Abstract: The system and methods of the disclosed subject matter provide an experimentation framework to allow a user to perform machine learning experiments on tenant data within a multi-tenant database system. The system may provide an experimental interface to allow modification of machine learning algorithms, machine learning parameters, and tenant data fields. The user may be prohibited from viewing any of the tenant data or may be permitted to view only a portion of the tenant data. Upon generating an experimental model using the experimental interface, the user may view results comparing the performance of the experimental model with a current production model.
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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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