Assessing accuracy of trained predictive models
First Claim
1. A computer-implemented method comprising:
- receiving a first data set of data samples, each data sample comprising input data and corresponding output data, wherein the first data set is new relative to (i) an initial training data set and (ii) a plurality of previously received update data sets of data samples, wherein the initial training data set was used to train each trained predictive model in a repository of trained predictive models, at least some which are updateable, and wherein the plurality of previously received update data sets of the data sample were used to retrain one or more updateable trained predictive models in the repository;
assigning a richness score to each of the data samples included in the first data set and to each of a set of retained data samples from the initial training data and the plurality of previously received update data sets, wherein the richness score for a particular data sample indicates how information rich the particular data sample is, relative to other data samples in the set of retained data samples and the first data set, for determining an accuracy of a trained predictive model;
ranking the data samples included in the first data set and the set of retained data samples based on the assigned richness scores;
selecting a first set of test data from the data samples included in the first data set and the set of retained data samples based on the ranking;
testing how accurate each of the trained predictive models in the repository is in determining predictive output data for given input data using the first set of test data and determining respective accuracy scores for each of the trained predictive models based on the testing; and
selecting a first trained predictive model from the repository based on the accuracy scores and providing access to the first trained predictive model to a client computing system for generating predictive output data based on input data received from the client computing system.
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Accused Products
Abstract
A system includes a computer(s) coupled to a data storage device(s) that stores a training data repository and a predictive model repository. The training data repository includes retained data samples from initial training data and from previously received data sets. The predictive model repository includes at least one updateable trained predictive model that was trained with the initial training data and retrained with the previously received data sets. A new data set is received. A richness score is assigned to each of the data samples in the set and to the retained data samples that indicates how information rich a data sample is for determining accuracy of the trained predictive model. A set of test data is selected based on ranking by richness score the retained data samples and the new data set. The trained predictive model is accuracy tested using the test data and an accuracy score determined.
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Citations
18 Claims
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1. A computer-implemented method comprising:
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receiving a first data set of data samples, each data sample comprising input data and corresponding output data, wherein the first data set is new relative to (i) an initial training data set and (ii) a plurality of previously received update data sets of data samples, wherein the initial training data set was used to train each trained predictive model in a repository of trained predictive models, at least some which are updateable, and wherein the plurality of previously received update data sets of the data sample were used to retrain one or more updateable trained predictive models in the repository; assigning a richness score to each of the data samples included in the first data set and to each of a set of retained data samples from the initial training data and the plurality of previously received update data sets, wherein the richness score for a particular data sample indicates how information rich the particular data sample is, relative to other data samples in the set of retained data samples and the first data set, for determining an accuracy of a trained predictive model; ranking the data samples included in the first data set and the set of retained data samples based on the assigned richness scores; selecting a first set of test data from the data samples included in the first data set and the set of retained data samples based on the ranking; testing how accurate each of the trained predictive models in the repository is in determining predictive output data for given input data using the first set of test data and determining respective accuracy scores for each of the trained predictive models based on the testing; and selecting a first trained predictive model from the repository based on the accuracy scores and providing access to the first trained predictive model to a client computing system for generating predictive output data based on input data received from the client computing system. - View Dependent Claims (2, 3, 4, 5, 6)
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7. A computer-implemented system comprising:
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one or more computers; and one or more data storage devices coupled to the one or more computers, storing; a training data repository that includes a set of retained data samples, wherein the set of retained data samples includes at least some data samples from an initial training data set and some data samples from a plurality of previously received update data sets, wherein each data sample includes input data and corresponding output data; a predictive model repository that includes trained predictive models that were each trained with the initial training data set, wherein at least some of the trained predictive models are updateable and were each retrained with the plurality of previously received update data sets, and instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising; receiving a first data set of data samples, each data sample comprising input data and corresponding output data, wherein the first data set is new relative to (i) the initial training data set and (ii) the plurality of previously received update data sets; assigning a richness score to each of the data samples included in the first data set and to each of the set of retained data samples included in the training data repository, wherein the richness score for a particular data sample indicates how information rich the particular data sample is, relative to other data samples in the set of retained data samples and the first data set, for determining an accuracy of a trained predictive model; ranking the data samples included in the first data set and the retained data samples based on the assigned richness scores; selecting a first set of test data from the data samples included in the first data set and the set of retained data samples based on the ranking; testing how accurate each of the trained predictive models in the repository is in determining predictive output data for given input data using the first set of test data and determining respective accuracy scores for each of the trained predictive models based on the testing; and selecting a first trained predictive model from the repository based on the accuracy scores and providing access to the first trained predictive model to a client computing system for generating predictive output data based on input data received from the client computing system. - View Dependent Claims (8, 9, 10, 11, 12)
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13. A computer-readable storage device encoded with a computer program product, the computer program product comprising instructions that when executed on one or more computers cause the one or more computers to perform operations comprising:
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receiving a first data set of data samples, each data sample comprising input data and corresponding output data, wherein the first data set is new relative to (i) an initial training data set and (ii) a plurality of previously received update data sets of data samples, wherein the initial training data set was used to train each trained predictive model in a repository of trained predictive models, at least some which are updateable, and wherein the plurality of previously received update data sets of the data sample were used to retrain one or more updateable trained predictive models in the repository; assigning a richness score to each of the data samples included in the first data set and to each of a set of retained data samples from the initial training data and the plurality of previously received update data sets, wherein the richness score for a particular data sample indicates how information rich the particular data sample is, relative to other data samples in the set of retained data samples and the first data set, for determining an accuracy of a trained predictive model; ranking the data samples included in the first data set and the set of retained data samples based on the assigned richness scores; selecting a first set of test data from the data samples included in the first data set and the set of retained data samples based on the ranking; testing how accurate each of the trained predictive models in the repository is in determining predictive output data for given input data using the first set of test data and determining respective accuracy scores for each of the trained predictive models based on the testing; and selecting a first trained predictive model from the repository based on the accuracy scores and providing access to the first trained predictive model to a client computing system for generating predictive output data based on input data received from the client computing system. - View Dependent Claims (14, 15, 16, 17, 18)
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Specification