Large scale machine learning systems and methods

1. A method comprising:

generating, by one or more processors, a model based on a plurality of features associated with documents that include spam documents and non-spam documents, the generating of the model including:

identifying, by the one or more processors, a condition associated with two or more features of the plurality of features,

receiving, by the one or more processors and from a plurality of devices associated with the documents, statistics associated with the identified condition, a particular statistic, of the received statistics, being received from a particular device, of the plurality of devices, and the particular statistic indicating a particular weight, associated with the identified condition, for the particular device,

generating a candidate rule for the model based on the condition and the received statistics,

determining whether to add the candidate rule to the model,

upon determining that the candidate rule should not be added to the model, setting a weight, for the candidate rule, to a value that indicates that the candidate rule should not be added to the model, and

generating, by the one or more processors and based on the received statistics, a composite weight associated with the condition, the composite weight indicating how relevant the condition is, with respect to other conditions, in determining whether a document is to be classified as spam, the other conditions being associated with respective subsets of the plurality of features that differ from the condition;

receiving, by the one or more processors, a particular document, the particular document being associated with one or more features of the plurality of features;

determining, by the one or more processors and based on applying the model to the one or more features, to classify the particular document as a spam document; and

storing, by the one or more processors, information regarding the particular document based on the particular document being classified as the spam document.

2. The method of claim 1 , the particular document including an e-mail.

3. The method of claim 1 , the particular statistic further identifying a particular subset of the documents associated with the particular device.

4. The method of claim 3 , the particular statistic further identifying one or more documents, of the particular subset of the documents associated with the particular device, that are classified, by the particular device, as spam documents.

5. The method of claim 1 , the generating of the model further including:

replacing, in the model, a previous weight associated with the condition with the composite weight.

6. The method of claim 5 , the generating of the model further including:

determining a cost associated with replacing, in the model, the previous weight with the composite weight; and

determining that the cost does not exceed a threshold cost, the previous weight being replaced with the composite weight based on the cost not exceeding the threshold cost.

7. The method of claim 1 , the generating of the model further including:

determining that the composite weight satisfies a threshold weight,

adding the candidate rule to the model based on the composite weight satisfying the threshold weight, and

notifying the plurality of devices that the candidate rule was added to the model.

8. A system comprising:

one or more processors to:

generate a model based on a plurality of features associated with documents that include spam documents and non-spam documents;

the one or more processors, when generating the model, being further to:

identify a condition associated with two or more features of the plurality of features,

receive, from a plurality of devices associated with the documents, statistics associated with the identified condition, a particular statistic, of the received statistics, being received from a particular device, of the plurality of devices, and the particular statistic indicating a particular weight, associated with the identified condition, for the particular device,

generate a candidate rule for the model based on the condition and the received statistics,

determine whether to add the candidate rule to the model, and

upon determining that the candidate rule should not be added to the model, set a weight, for the candidate rule, to a value that indicates that the candidate rule should not be added to the model, and

generate, based on the received statistics, a composite weight associated with the condition, the composite weight indicating how relevant the condition is, with respect to other conditions, in determining whether a document is to be classified as spam, the other conditions being associated with respective subsets of the plurality of features that differ from the condition;

receive a particular document, the particular document being associated with one or more features of the plurality of features;

classify, based on applying the model to the one or more features, the particular document as a spam document; and

processing the particular document based on classifying the particular document as the spam document.

9. The system of claim 8 , the particular document including an e-mail.

10. The system of claim 8 , the particular statistic further identifying a particular subset of the documents associated with the particular device.

11. The system of claim 10 , the particular statistic further identifying one or more documents, of the particular subset of the documents associated with the particular device, that are classified, by the particular device, as spam documents.

12. The system of claim 8 , the one or more processors, when generating the model, being further to:

replace, in the model, a previous weight, associated with the condition, with the composite weight.

13. The system of claim 12 , the one or more processors, when generating the model, being further to:

determine a cost associated with the replacing, in the model, the previous weight with the composite weight; and

determine that the cost does not exceed a threshold cost,

the one or more processors replacing the previous weight with the composite weight based on the cost not exceeding the threshold cost.

14. The system of claim 8 , the one or more processors, when generating the model, being further to:

determine that the composite weight satisfies a threshold weight,

add the candidate rule to the model based on the composite weight satisfying the threshold weight, and

notify the plurality of devices that the candidate rule was added to the model.

15. A non-transitory memory device, comprising:

one or more instructions which, when executed by one or more processors, cause the one or more processors to:

identify a condition associated with two or more features, of a plurality of features associated with documents that include spam documents and non-spam documents;

receive, from a plurality of devices associated with the documents, statistics associated with the identified condition, a particular statistic, of the received statistics, being received from a particular device, of the plurality of devices, and the particular statistic indicating a particular weight, associated with the identified condition, for the particular device;

generate a candidate rule for the model based on the condition and the received statistics;

determine whether to add the candidate rule to the model;

upon determining that the candidate rule should not be added to the model, set a weight, for the candidate rule, to a value that indicates that the candidate rule should not be added to the model;

generate, based on the received statistics, a composite weight associated with the condition, the composite weight indicating how relevant the condition is, with respect to other conditions, in determining whether a document is to be classified as spam, the other conditions being associated with respective subsets of the plurality of features that differ from the condition;

receive a particular document, the particular document being associated with one or more features of the plurality of features; and

classify, based on applying the composite weight to the one or more features, the particular document as a spam document.

16. The non-transitory memory device of claim 15 , the particular statistic further identifying a particular subset of the documents associated with the particular device.

17. The non-transitory memory device of claim 16 , the particular statistic further identifying one or more documents, of the particular subset of the documents associated with the particular device, that are classified, by the particular device, as spam documents.

18. The non-transitory memory device of claim 15 , the one or more instructions further causing the one or more processors to:

replace a previous weight, associated with the condition, with the composite weight.

19. The non-transitory memory device of claim 18 , the one or more instructions, when causing the one or more processors to replace the previous weight with the composite weight, further causing the one or more processors to:

determine a cost associated with replacing the previous weight with the composite weight;

determine that the cost does not exceed a threshold cost; and

replace the previous weight with the composite weight based on the cost not exceeding the threshold cost.

20. The non-transitory memory device of claim 15 , the one or more instructions further causing the one or more processors to:

determine that the composite weight satisfies a threshold weight,

store the candidate rule based on the composite weight satisfying the threshold weight, and

notify the plurality of devices that the candidate rule was stored.