Predictive analytic modeling platform
First Claim
1. A computer-implemented method comprising:
- receiving over a network predictive modeling training data from a client computing system;
partitioning the training data into a plurality of subsamples;
using the plurality of subsamples and a plurality of training functions obtained from a repository of training functions to train a plurality of predictive models using cross-validation;
generating a cross-validation score for each of the plurality of trained predictive models, where each cross-validation score indicates the accuracy of the respective trained predictive model;
selecting a first trained predictive model from among the plurality of trained predictive models using the generated cross-validation scores; and
providing access to the first trained predictive model over the network.
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Accused Products
Abstract
Methods, systems, and apparatus, including computer programs encoded on one or more computer storage devices, for training a predictive model. In one aspect, a method includes receiving over a network predictive modeling training data from a client computing system. The training data and multiple training functions obtained from a repository of training functions are used to train multiple predictive models. A score is generated for each of the trained predictive models, where each score represents an estimation of the effectiveness of the respective trained predictive model. A first trained predictive model is selected from among the trained predictive models based on the generated scores. Access to the first trained predictive model is provided to the client computing system.
165 Citations
20 Claims
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1. A computer-implemented method comprising:
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receiving over a network predictive modeling training data from a client computing system; partitioning the training data into a plurality of subsamples; using the plurality of subsamples and a plurality of training functions obtained from a repository of training functions to train a plurality of predictive models using cross-validation; generating a cross-validation score for each of the plurality of trained predictive models, where each cross-validation score indicates the accuracy of the respective trained predictive model; selecting a first trained predictive model from among the plurality of trained predictive models using the generated cross-validation scores; and providing access to the first trained predictive model over the network. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A system, comprising:
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a data processing apparatus; and a non-transitory computer readable storage medium in data communication with the data processing apparatus and storing instructions executable by the data processing apparatus and upon such execution cause the data processing to perform operations comprising; receiving over a network predictive modeling training data from a client computing system; partitioning the training data into a plurality of subsamples; using the plurality of subsamples and a plurality of training functions obtained from a repository of training functions to train a plurality of predictive models using cross-validation; generating a cross-validation score for each of the plurality of trained predictive models, where each cross-validation score indicates the accuracy of the respective trained predictive model; selecting a first trained predictive model from among the plurality of trained predictive models using the generated cross-validation scores; and providing access to the first trained predictive model over the network. - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A non-transitory computer readable storage medium storing instructions executable by a data processing apparatus and upon such execution cause the data processing to perform operations comprising:
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receiving over a network predictive modeling training data from a client computing system; partitioning the training data into a plurality of subsamples; using the plurality of subsamples and a plurality of training functions obtained from a repository of training functions to train a plurality of predictive models using cross-validation; generating a cross-validation score for each of the plurality of trained predictive models, where each cross-validation score indicates the accuracy of the respective trained predictive model; selecting a first trained predictive model from among the plurality of trained predictive models using the generated cross-validation scores; and providing access to the first trained predictive model over the network. - View Dependent Claims (20)
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Specification