MACHINE LEARNING SEMANTIC MODEL
First Claim
1. A computer-implemented method, the method comprising:
- specifying a business problem to determine a probability of an event occurring in which the business problem includes a constraint;
selecting a data source for a predictive model associated with a predictive algorithm in which the predictive model includes one or more queries and parameters;
determining a set of transformations based on the queries and parameters for at least a subset of data from the data source to be processed by the predictive algorithm;
identifying a set of patterns based on the set of transformations for at least the subset of data from the data source; and
providing a trained predictive model including the determined set of patterns, the set of transformations, and the associated predictive algorithm for solving the specified business problem.
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Abstract
The subject technology discloses configurations for creating reusable predictive models for applying to one or more data sources. The subject technology specifies a business problem to determine a probability of an event occurring. The business problem may include a constraint. A data source is selected for a predictive model associated with a predictive algorithm in which the predictive model includes one or more queries and parameters. A set of transformations are then determined based on the queries and parameters for at least a subset of data from the data source to be processed by the predictive algorithm. The subject technology identifies a set of patterns based on the set of transformations for at least the subset of data from the data source. A trained predictive model is then provided including the determined set of patterns, the set of transformations, and the associated predictive algorithm for solving the specified business problem.
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Citations
24 Claims
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1. A computer-implemented method, the method comprising:
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specifying a business problem to determine a probability of an event occurring in which the business problem includes a constraint; selecting a data source for a predictive model associated with a predictive algorithm in which the predictive model includes one or more queries and parameters; determining a set of transformations based on the queries and parameters for at least a subset of data from the data source to be processed by the predictive algorithm; identifying a set of patterns based on the set of transformations for at least the subset of data from the data source; and providing a trained predictive model including the determined set of patterns, the set of transformations, and the associated predictive algorithm for solving the specified business problem. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16)
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17. A computer-implemented method, the method comprising:
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selecting a data source for a trained predictive model in which the trained predictive model includes a set of patterns, a set of transformations, and is associated with a predictive algorithm for solving a business problem; applying the set of patterns according to the predictive algorithm to return a set of data from the data source; performing the set of transformations on the set of data; and providing a score indicating a probability of an event specified by the business problem based on the predictive algorithm on the set of data.
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18. A computer-implemented method, the method comprising:
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receiving a score corresponding to a predictive model for solving a business problem; converting the score into a semantically meaningful format for an end-user; and providing the converted score to the end-user. - View Dependent Claims (19, 20)
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21. A system, the system comprising:
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one or more processors; a memory comprising instructions stored therein, which when executed by the one or more processors, cause the processors to perform operations comprising; specifying a business problem to determine a probability of an event occurring in which the business problem includes a constraint; selecting a data source for a predictive model associated with a predictive algorithm in which the predictive model includes one or more queries and parameters; determining a set of transformations based on the queries and parameters for at least a subset of data from the data source to be processed by the predictive algorithm; identifying a set of patterns based on the set of transformations for at least the subset of data from the data source; and providing a trained predictive model including the determined set of patterns, the set of transformations, and the associated predictive algorithm for solving the specified business problem. - View Dependent Claims (22, 23)
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24. A non-transitory machine-readable medium comprising instructions stored therein, which when executed by a machine, cause the machine to perform operations comprising:
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specifying a business problem to determine a probability of an event occurring in which the business problem includes a constraint; selecting a data source for a predictive model associated with a predictive algorithm in which the predictive model includes one or more queries and parameters; determining a set of transformations based on the queries and parameters for at least a subset of data from the data source to be processed by the predictive algorithm; identifying a set of patterns based on the set of transformations for at least the subset of data from the data source; and providing a trained predictive model including the determined set of patterns, the set of transformations, and the associated predictive algorithm for solving the specified business problem.
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Specification