Determining a Type of Predictive Model for Training Data

• US 20150170048A1
• Filed: 08/12/2011
• Published: 06/18/2015
• Est. Priority Date: 08/12/2011
• Status: Abandoned Application

• Current Assignee: Google LLC (Alphabet Inc.)
• Original Assignee: Google LLC (Alphabet Inc.)
• Inventors: Lin, Wei-Hao, Green, Travis H.K., Kaplow, Robert, Fu, Gang, Mann, Gideon S.
• Application Number:
  US13/209,358
• Publication Number:
  US 20150170048A1
• Time in Patent Office: Days
• US Class Current: 1/1
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: H04L 51/212 using filtering or selective blocking

Abstract

A computer-implemented method includes receiving, in a system of one or more computers, training data for predictive modeling, the training data including a plurality of categories; determining, by the system, one or more attributes of the training data; identifying, by the system in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes; obtaining a utility function for the predictive model of the identified type, the utility function specifying importance of the plurality of categories relative to each other; and generating, based on the training data and the utility function, a predictive model of the identified type.

Claims

1. A computer-implemented method comprising:

receiving, in a system of one or more computers, a set of training data for predictive modeling;

determining, by the system, one or more attributes of the training data;

identifying, by the system in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;

obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;

assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and

training, based on assigning the first and second weighted values, a predictive model of the identified type.

3. The computer-implemented method of claim 1, wherein training comprises:

determining one or more patterns in the training data.

4. The computer-implemented method of claim 1, wherein the identified type of predictive model comprises one or more of a k-nearest neighbor predictive model, a logistic regression predictive model, a Naive Bayes predictive model, and a support vector predictive model.

5. The computer-implemented method of claim 1, wherein the one or more attributes comprise one or more of a size of the training data, an estimate of an amount of time required to train a predictive model with the training data, a number of categories in the training data, and a number of features of the training data.

6. The computer-implemented method of claim 1, wherein the predictive model of the identified type comprises a k-nearest neighbor predictive model, and wherein the method further comprises:

receiving input data;

identifying, from the k-nearest neighbor predictive model, a k-nearest item of data that is closest to the input data, relative to closeness of other k-nearest items of data to the input data; and

assigning to the input data a category associated with the identified k-nearest item data.

2. (canceled)

7. A system comprising:

one or more computers; and

one or more storage devices storing instructions that are executable by the one or more computers to perform operations comprising;

receiving a set of training data for predictive modeling;

determining one or more attributes of the training data;

identifying, in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;

obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;

assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and

training, based on assigning the first and second weighted values, a predictive model of the identified type.

9. The system of claim 7, wherein training comprises:

determining one or more patterns in the training data.

10. The system of claim 7, wherein the identified type of predictive model comprises one or more of a k-nearest neighbor predictive model, a logistic regression predictive model, a Naive Bayes predictive model, and a support vector predictive model.

11. The system of claim 7, wherein the one or more attributes comprise one or more of a size of the training data, an estimate of an amount of time required to train a predictive model with the training data, a number of categories in the training data, and a number of features of the training data.

12. The system of claim 7, wherein the predictive model of the identified type comprises a k-nearest neighbor predictive model, and wherein the operations further comprise:

receiving input data;

identifying, from the k-nearest neighbor predictive model, a k-nearest item of data that is closest to the input data, relative to closeness of other k-nearest items of data to the input data; and

assigning to the input data a category associated with the identified k-nearest item data.

8. (canceled)

13. One or more storage devices storing instructions that are executable by one or more computers to perform operations comprising:

receiving a set of training data for predictive modeling;

determining one or more attributes of the training data;

identifying, in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;

obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;

assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and

training, based on assigning the first and second weighted values, a predictive model of the identified type.

15. The one or more storage devices of claim 13, wherein training comprises:

determining one or more patterns in the training data.

16. The one or more storage devices of claim 13, wherein the identified type of predictive model comprises one or more of a k-nearest neighbor predictive model, a logistic regression predictive model, a Naive Bayes predictive model, and a support vector predictive model.

17. The one or more storage devices of claim 13, wherein the one or more attributes comprise one or more of a size of the training data, an estimate of an amount of time required to train a predictive model with the training data, a number of categories in the training data, and a number of features of the training data.

18. The one or more storage devices of claim 13, wherein the predictive model of the identified type comprises a k-nearest neighbor predictive model, and wherein the operations further comprise:

receiving input data;

identifying, from the k-nearest neighbor predictive model, a k-nearest item of data that is closest to the input data, relative to closeness of other k-nearest items of data to the input data; and

assigning to the input data a category associated with the identified k-nearest item data.

14. (canceled)

19. A system comprising:

means for receiving a set of training data for predictive modeling;

means for determining one or more attributes of the training data;

means for identifying, in a mapping of attributes to types of predictive models, a type of predictive model that is mapped to at least one of the one or more attributes;

means for obtaining a utility function that specifies a first weighted value to be applied to a first item of training data in the set and that further specifies a second, different weighted value to be applied to a second, different item of training data in the set, with a weighted value for an item of training data specifying an importance of the item of training data, relative to another importance of another item of training data;

means for assigning, based on the utility function, (i) the first weighted value to the first item of training data in the set, and (ii) the second, different weighted value to the second, different item of training data in the set; and

means for training, based on assigning the first and second weighted values, a predictive model of the identified type.