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

IPC: G06N20/00 ,  G06N5/04

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.

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

IPC: G06N20/00 ,  G06N5/04

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.

公开(公告)号：US20200057959A1

公开(公告)日：2020-02-20

申请号：US16264659

申请日：2019-01-31

Applicant: salesforce.com, inc.

Inventor： Kevin Moore ,  Leah McGuire ,  Matvey Tovbin ,  Mayukh Bhaowal ,  Shubha Nabar

IPC: G06N20/00

Abstract: Instances of data associated with hindsight bias in a training set of data for a machine learning system can be reduced. A first set of data, having a first set of fields, can be received. Data in a first field can be analyzed with respect to data in a second field corresponding to an event to be predicted. A result can be that the data in the first field is associated with hindsight bias. A second set of data, having a second set of fields, can be produced. The second set of fields can exclude the first field. One or more features associated with the second set of data can be generated. A third set of data, having the second set of fields and fields that correspond to the one or more features, can be produced. The training set of data can be produced using the third set of data.