Predictive Analytical Modeling Accuracy Assessment

• US 20120284212A1
• Filed: 05/04/2011
• Published: 11/08/2012
• Est. Priority Date: 05/04/2011
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google Inc. (Alphabet Inc.)
• Inventors: Lin, Wei-Hao, Green, Travis, Kaplow, Robert, Fu, Gang, Mann, Gideon S.
• Application Number:
  US13/101,040
• Publication Number:
  US 20120284212A1
• Time in Patent Office: Days
• US Class Current: 706/12
• CPC Class Codes: G06N 20/00 Machine learning

Abstract

A system includes a computer(s) coupled to a data storage device(s) that stores a training function repository and a predictive model repository that includes includes updateable trained predictive models each associated with an accuracy score. A series of training data sets are received, being training samples each having output data that corresponds to input data. The training data is different from initial training data that was used with training functions from the repository to train the predictive models initially. Upon receiving a first training data set included in the series and for each predictive model in the repository, the input data in the first training set is used to generate predictive output data that is compared to the output data. Based on the comparison and previous comparisons determined from the initial training data and from previously received training data sets, an updated accuracy score for each predictive model is determined.

Claims

1. A computer-implemented system comprising:

one or more computers; and

one or more data storage devices coupled to the one or more computers, storing;

a repository of training functions,

a predictive model repository of trained predictive models, including a plurality of updateable trained predictive models, and wherein each trained predictive model is associated with an accuracy score that represents an estimation of the accuracy of the respective trained predictive model, and

instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising;

receiving over a network a series of training data sets for predictive modeling from a client computing system, wherein training data included in the training data sets includes training samples that each comprise output data that corresponds to input data and wherein the training data included in the training data sets is different from initial training data that was used with a plurality of training functions obtained from the repository to train the trained predictive models stored in the predictive model repository;

upon receiving a first training data set included in the series of training data sets and for each trained predictive model in the predictive model repository, using the input data included in the first training data set to generate predictive output data and comparing the predictive output data to the output data included in the first training data set, and based on the comparison and previous comparisons that were determined from the initial training data and from previously received training data sets, determining an updated accuracy score for association with each trained predictive model in the repository;

for each updateable trained predictive model in the predictive model repository, using the first training data set, a first training function obtained from the repository of training functions that was used to generate the updateable trained predictive model and using the updateable trained predictive model, to generate a retrained predictive model and replacing the updateable trained predictive model in the predictive model repository with the retrained predictive model;

selecting a first trained predictive model from among the plurality of trained predictive models and retrained predictive models included in the predictive model repository based on the determined updated accuracy scores; and

providing access to the first trained predictive model over the network.

2. The system of claim 1, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

adding the sum of correct predictive outputs to previously determined sums of correct predictive outputs that were determined when the initial training data and other training data sets in the series of training data sets were received to determine a total number of correct predictive outputs; and

dividing the total number of correct predictive outputs by a sum of the number of training samples included in the first training data set added to the number of training samples included in the initial training data and the other training data sets.

3. The system of claim 1, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

weighting the sum of corrective predictive outputs with a first weight that is determined based on time of receipt of the first training data set;

adding the weighted sum of correct predictive outputs to previously determined weighted sums of correct predictive outputs that were determined when the initial training data and other training data sets in the series of training data sets were received to determine a total number of correct predictive outputs, wherein each weighted sum is weighted based on a time of receipt of corresponding training data; and

dividing the total number of correct predictive outputs by the number of training samples included in the first training data set weighted by the first weight summed with the numbers of training samples included in the initial training data and the other training data sets, where each of the numbers of training samples is weighted according to the same weight as its corresponding sum of predictive outputs.

4. The system of claim 1, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

identifying which training data sets from the initial training data and from the series of training data sets were received within a predetermined time-based window;

adding the sum of correct predictive outputs to previously determined sums of correct predictive outputs that were determined when the identified training data sets were each received to determine a total number of correct predictive outputs; and

dividing the total number of correct predictive outputs by a sum of the number of training samples included in the first training data set added to the number of training samples included in the identified training data sets.

5. The system of claim 4, wherein the predetermined time-based window indicates a discrete period of time during which the training data sets must have been received to be included in the identified training data sets.

6. The system of claim 4, wherein the predetermined time-based window indicates a discrete number of most recently received training data sets that are to be included in the identified training data sets.

7. A computer-implemented method comprising:

receiving over a network a series of training data sets for predictive modeling from a client computing system, wherein training data included in the training data sets includes training samples that each comprise output data that corresponds to input data and wherein the training data included in the training data sets is different from initial training data that was used with a plurality of training functions obtained from a repository of training functions to train a plurality of trained predictive models stored in a predictive model repository wherein each trained predictive model is associated with an accuracy that indicates an accuracy of the trained predictive model in generating predictive outputs;

upon receiving a first training data set included in the series of training data sets and for each trained predictive model in the predictive model repository, using the input data included in the first training data set to generate predictive output data and comparing the predictive output data to the output data included in the first training data set, and based on the comparison and previous comparisons that were determined from the initial training data and from previously received training data sets, determining an updated accuracy score for association with each trained predictive model in the repository;

for each updateable trained predictive model in the predictive model repository, using the first training data set, a first training function obtained from the repository of training functions that was used to generate the updateable trained predictive model and using the updateable trained predictive model, to generate a retrained predictive model and replacing the updateable trained predictive model in the predictive model repository with the retrained predictive model;

selecting a first trained predictive model from among the plurality of trained predictive models and retrained predictive models included in the predictive model repository based on the determined updated accuracy scores; and

providing access to the first trained predictive model over the network.

8. The method of claim 7, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

adding the sum of correct predictive outputs to previously determined sums of correct predictive outputs that were determined when the initial training data and other training data sets in the series of training data sets were received to determine a total number of correct predictive outputs; and

dividing the total number of correct predictive outputs by a sum of the number of training samples included in the first training data set added to the number of training samples included in the initial training data and the other training data sets.

9. The method of claim 7, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

weighting the sum of corrective predictive outputs with a first weight that is determined based on time of receipt of the first training data set;

adding the weighted sum of correct predictive outputs to previously determined weighted sums of correct predictive outputs that were determined when the initial training data and other training data sets in the series of training data sets were received to determine a total number of correct predictive outputs, wherein each weighted sum is weighted based on a time of receipt of corresponding training data; and

dividing the total number of correct predictive outputs by the number of training samples included in the first training data set weighted by the first weight summed with the numbers of training samples included in the initial training data and the other training data sets, where each of the numbers of training samples is weighted according to the same weight as its corresponding sum of predictive outputs.

10. The method of claim 7, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

identifying which training data sets from the initial training data and from the series of training data sets were received within a predetermined time-based window;

adding the sum of correct predictive outputs to previously determined sums of correct predictive outputs that were determined when the identified training data sets were each received to determine a total number of correct predictive outputs; and

dividing the total number of correct predictive outputs by a sum of the number of training samples included in the first training data set added to the number of training samples included in the identified training data sets.

11. The method of claim 10, wherein the predetermined time-based window indicates a discrete period of time during which the training data sets must have been received to be included in the identified training data sets.

12. The method of claim 10, wherein the predetermined time-based window indicates a discrete number of most recently received training data sets that are to be included in the identified training data sets.

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:

receiving over a network a series of training data sets for predictive modeling from a client computing system, wherein training data included in the training data sets includes training samples that each comprise output data that corresponds to input data and wherein the training data included in the training data sets is different from initial training data that was used with a plurality of training functions obtained from a repository of training functions to train a plurality of trained predictive models stored in a predictive model repository wherein each trained predictive model is associated with an accuracy that indicates an accuracy of the trained predictive model in generating predictive outputs;

upon receiving a first training data set included in the series of training data sets and for each trained predictive model in the predictive model repository, using the input data included in the first training data set to generate predictive output data and comparing the predictive output data to the output data included in the first training data set, and based on the comparison and previous comparisons that were determined from the initial training data and from previously received training data sets, determining an updated accuracy score for association with each trained predictive model in the repository;

for each updateable trained predictive model in the predictive model repository, using the first training data set, a first training function obtained from the repository of training functions that was used to generate the updateable trained predictive model and using the updateable trained predictive model, to generate a retrained predictive model and replacing the updateable trained predictive model in the predictive model repository with the retrained predictive model;

selecting a first trained predictive model from among the plurality of trained predictive models and retrained predictive models included in the predictive model repository based on the determined updated accuracy scores; and

providing access to the first trained predictive model over the network.

14. The computer-readable storage device of claim 13, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

adding the sum of correct predictive outputs to previously determined sums of correct predictive outputs that were determined when the initial training data and other training data sets in the series of training data sets were received to determine a total number of correct predictive outputs; and

dividing the total number of correct predictive outputs by a sum of the number of training samples included in the first training data set added to the number of training samples included in the initial training data and the other training data sets.

15. The computer-readable storage device of claim 13, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

weighting the sum of corrective predictive outputs with a first weight that is determined based on time of receipt of the first training data set;

adding the weighted sum of correct predictive outputs to previously determined weighted sums of correct predictive outputs that were determined when the initial training data and other training data sets in the series of training data sets were received to determine a total number of correct predictive outputs, wherein each weighted sum is weighted based on a time of receipt of corresponding training data; and

dividing the total number of correct predictive outputs by the number of training samples included in the first training data set weighted by the first weight summed with the numbers of training samples included in the initial training data and the other training data sets, where each of the numbers of training samples is weighted according to the same weight as its corresponding sum of predictive outputs.

16. The computer-readable storage device of claim 13, wherein determining the updated accuracy score for a particular trained predictive model comprises:

summing a number of correct predictive outputs included in the generated predictive output data as determined from the comparison;

identifying which training data sets from the initial training data and from the series of training data sets were received within a predetermined time-based window;

adding the sum of correct predictive outputs to previously determined sums of correct predictive outputs that were determined when the identified training data sets were each received to determine a total number of correct predictive outputs; and

dividing the total number of correct predictive outputs by a sum of the number of training samples included in the first training data set added to the number of training samples included in the identified training data sets.

17. The computer-readable storage device of claim 16, wherein the predetermined time-based window indicates a discrete period of time during which the training data sets must have been received to be included in the identified training data sets.

18. The computer-readable storage device of claim 16, wherein the predetermined time-based window indicates a discrete number of most recently received training data sets that are to be included in the identified training data sets.