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1. 发明授权

US10366346B2 Systems and techniques for determining the predictive value of a feature 有权
公开(公告)号：US10366346B2
公开(公告)日：2019-07-30
申请号：US15331797
申请日：2016-10-21
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Xavier Conort , Sergey Yurgenson , Mark L. Steadman , Glen Koundry
IPC分类号： G06F15/18 , G06N20/00 , G06Q10/04 , G06Q10/06 , G06Q30/02
摘要： A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.

2. 发明申请

US20200257992A1 SYSTEMS FOR TIME-SERIES PREDICTIVE DATA ANALYTICS, AND RELATED METHODS AND APPARATUS 审中-公开
公开(公告)号：US20200257992A1
公开(公告)日：2020-08-13
申请号：US16682813
申请日：2019-11-13
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Xavier Conort , Mark L. Steadman , Peter Prettenhofer
IPC分类号： G06N5/04 , G06N20/20 , G06N20/10 , G06N20/00 , G06N5/02 , G06F9/50 , G06Q10/04 , G06Q10/06
摘要： A predictive modeling method may include determining a time interval of time-series data; identifying one or more variables of the data as targets; determining a forecast range and a skip range associated with a prediction problem represented by the data; generating training data and testing data from the time-series data; fitting a predictive model to the training data; and testing the fitted model on the testing data. The forecast range may indicate a duration of a period for which values of the targets are to be predicted. The skip range may indicate a temporal lag between the time period corresponding to the data used to make predictions and the time period corresponding to the predictions. The skip range may separate input data subsets representing model inputs from subsets representing model outputs, and separate test data subsets representing model inputs from subsets representing validation data.

3. 发明申请

US20200090075A1 SYSTEMS AND TECHNIQUES FOR DETERMINING THE PREDICTIVE VALUE OF A FEATURE 审中-公开
公开(公告)号：US20200090075A1
公开(公告)日：2020-03-19
申请号：US16447924
申请日：2019-06-20
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Xavier Conort , Sergey Yurgenson , Mark L. Steadman , Glen Koundry
IPC分类号： G06N20/00 , G06Q10/04 , G06Q10/06 , G06Q30/02
摘要： A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.

4. 发明申请

US20180060738A1 SYSTEMS AND TECHNIQUES FOR DETERMINING THE PREDICTIVE VALUE OF A FEATURE 审中-公开
公开(公告)号：US20180060738A1
公开(公告)日：2018-03-01
申请号：US15331797
申请日：2016-10-21
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Timothy Owen , Xavier Conort
IPC分类号： G06N5/02 , G06N99/00 , G06N7/00
CPC分类号： G06N20/00 , G06Q10/04 , G06Q10/06 , G06Q10/067 , G06Q30/0201
摘要： A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.

5. 发明授权

US11514369B2 Systems and methods for machine learning model interpretation 有权
公开(公告)号：US11514369B2
公开(公告)日：2022-11-29
申请号：US17345405
申请日：2021-06-11
申请人： DataRobot, Inc.
发明人： Mark Benjamin Romanowsky , Jared Bowns , Thomas Whitehead , Thomas Stearns , Xavier Conort , Anastasiia Tamazlykar , Mohak Saxena
IPC分类号： G06N20/00 , G06N5/04 , G10L15/197
摘要： Systems and methods are described for interpreting machine learning model predictions. An example method includes: providing a machine learning model configured to receive a plurality of features as input and provide a prediction as output, wherein the plurality of features includes an engineered feature including a combination of two or more parent features; calculating a Shapley value for each feature in the plurality of features; and allocating a respective portion of the Shapley value for the engineered feature to each of the two or more parent features.

6. 发明申请

US20160364647A1 SYSTEMS AND TECHNIQUES FOR PREDICTIVE DATA ANALYTICS 有权
公开(公告)号：US20160364647A1
公开(公告)日：2016-12-15
申请号：US15217640
申请日：2016-07-22
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Timothy Owen , Xavier Conort
IPC分类号： G06N5/04 , G06N99/00
CPC分类号： G06Q10/067 , G06F9/5011 , G06N5/02 , G06N5/04 , G06N99/005 , G06Q10/04 , G06Q10/06
摘要： Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.

7. 发明申请

US20150339572A1 SYSTEMS AND TECHNIQUES FOR PREDICTIVE DATA ANALYTICS 有权
标题翻译：用于预测数据分析的系统和技术
公开(公告)号：US20150339572A1
公开(公告)日：2015-11-26
申请号：US14720079
申请日：2015-05-22
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Timothy Owen , Xavier Conort
IPC分类号： G06N5/02 , G06F9/50
CPC分类号： G06Q10/067 , G06F9/5011 , G06N5/02 , G06N5/04 , G06N99/005 , G06Q10/04 , G06Q10/06
摘要： Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.
摘要翻译：描述了用于预测数据分析的系统和技术。在用于选择预测问题的预测模型的方法中，可以基于预测问题的特性和/或各个建模过程的属性来确定用于预测问题的预测建模过程的适用性。可以基于所选择的用于预测问题的建模过程的适合性来选择预测建模过程的子集。可以基于所选择的用于预测问题的建模过程的适合性来生成分配用于执行所选建模过程的计算资源的资源分配调度。可以获得根据资源分配调度执行所选建模过程的结果。可以基于这些结果来选择预测问题的预测模型。

8. 发明申请

US20230067026A1 AUTOMATED DATA ANALYTICS METHODS FOR NON-TABULAR DATA, AND RELATED SYSTEMS AND APPARATUS 有权
公开(公告)号：US20230067026A1
公开(公告)日：2023-03-02
申请号：US17800173
申请日：2021-02-17
申请人： DataRobot, Inc.
发明人： Yurii Huts , Chin Ee Kin , Anton Kasyanov , Zachary Albert Mayer , Xavier Conort , Hon Nian Chua , Sabari Shanmugam , Atanas Mitkov Atanasov , Ivan Richard Pyzow
IPC分类号： G06V10/82 , G06V10/771 , G06V30/422
摘要： Automated data analytics techniques for non-tabular data sets may include methods and systems for (1) automatically developing models that perform tasks in the domains of computer vision, audio processing, speech processing, text processing, or natural language processing; (2) automatically developing models that analyze heterogeneous data sets containing image data and non-image data, and/or heterogeneous data sets containing tabular data and non-tabular data; (3) determining the importance of an image feature with respect to a modeling task, (4) explaining the value of a modeling target based at least in part on an image feature, and (5) detecting drift in image data. In some cases, multi-stage models may be developed, wherein a pre-trained feature extraction model extracts low-, mid-, high-, and/or highest-level features of non-tabular data, and a data analytics models uses those features (or features derived therefrom) to perform a data analytics task.

9. 发明申请

US20210390457A1 SYSTEMS AND METHODS FOR MACHINE LEARNING MODEL INTERPRETATION 有权
公开(公告)号：US20210390457A1
公开(公告)日：2021-12-16
申请号：US17345405
申请日：2021-06-11
申请人： DataRobot, Inc.
发明人： Mark Benjamin Romanowsky , Jared Bowns , Thomas Whitehead , Thomas Stearns , Xavier Conort , Anastasiia Tamazlykar , Mohak Saxena
IPC分类号： G06N20/00 , G06N5/04
摘要： Systems and methods are described for interpreting machine learning model predictions. An example method includes: providing a machine learning model configured to receive a plurality of features as input and provide a prediction as output, wherein the plurality of features includes an engineered feature including a combination of two or more parent features; calculating a Shapley value for each feature in the plurality of features; and allocating a respective portion of the Shapley value for the engineered feature to each of the two or more parent features.

10. 发明申请

US20180060744A1 SYSTEMS FOR SECOND-ORDER PREDICTIVE DATA ANALYTICS, AND RELATED METHODS AND APPARATUS 审中-公开
公开(公告)号：US20180060744A1
公开(公告)日：2018-03-01
申请号：US15790756
申请日：2017-10-23
申请人： DataRobot, Inc.
发明人： Jeremy Achin , Thomas DeGodoy , Timothy Owen , Xavier Conort
IPC分类号： G06N5/04 , G06N99/00 , G06Q10/06 , G06N5/02 , G06F9/50 , G06Q10/04
摘要： A predictive modeling method may include obtaining a fitted, first-order predictive model configured to predict values of output variables based on values of first input variables; and performing a second-order modeling procedure on the fitted, first-order model, which may include: generating input data including observations including observed values of second input variables and predicted values of the output variables; generating training data and testing data from the input data; generating a fitted second-order model of the fitted first-order model by fitting a second-order model to the training data; and testing the fitted, second-order model of the first-order model on the testing data. Each observation of the input data may be generated by (1) obtaining observed values of the second input variables, and (2) applying the first-order predictive model to corresponding observed values of the first input variables to generate the predicted values of the output variables.

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