Methods, systems and apparatus to improve multi-demographic modeling efficiency

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to improve multi-demographic modeling efficiency. An example apparatus includes a feature set aggregator to segregate training data based on feature sets of interest, and to identify households that participate in at least one of the feature sets of interest, a class enumerator to reduce multi-demographic model iterations by enumerating demographic combinations for the identified households, the enumerated demographic combinations including a single identifier to represent a combination of two or more demographic categories, and a modeling engine to generate training coefficients associated with respective ones of the enumerated demographic combinations.

1 . (canceled) 
     
     
         2 .- 20 . (canceled) 
     
     
         21 . An apparatus to reduce computational resources for a multi-demographic training model, comprising:
 memory; and   at least one processor to execute computer readable instructions to at least:
 reduce multi-demographic model iterations by enumerating demographic combinations for households that access at least one of a plurality of features represented in training data, the enumerated demographic combinations including an identifier, the identifier to represent a combination of two or more demographic categories and to include cartesian representations corresponding to occurrences or non-occurrences of a demographic category of interest, the demographic category of interest to be selected from the two or more demographic categories; and 
 generate probability values for ones of the enumerated demographic combinations based on training coefficients, the probability values to represent likelihoods that the ones of the enumerated demographic combinations accessed the at least one of the plurality of features, the training coefficients corresponding to the ones of the enumerated demographic combinations, the ones of the enumerated demographic combinations associated with (a) a training model and (b) a third-party model. 
   
     
     
         22 . The apparatus as defined in  claim 21 , wherein the at least one processor is to execute the computer readable instructions to identify ones of the households that access the at least one of the features of interest. 
     
     
         23 . The apparatus as defined in  claim 22 , wherein the at least one processor is to execute the computer readable instructions to associate demographic combinations with the ones of the households. 
     
     
         24 . The apparatus as defined in  claim 21 , wherein the at least one processor is to execute the computer readable instructions to generate the training coefficients based on a multinomial logistic regression with: (a) the training data, and (b) the ones of the enumerated demographic combinations. 
     
     
         25 . The apparatus as defined in  claim 21 , wherein the at least one processor is to execute the computer readable instructions to revert the probability values for respective ones of the enumerated demographic combinations to second probability values associated with individual demographic components. 
     
     
         26 . The apparatus as defined in  claim 21 , wherein the at least one processor is to execute the computer readable instructions to determine probability value differences between the training model and the third-party model. 
     
     
         27 . The apparatus as defined in  claim 26 , wherein the at least one processor is to execute the computer readable instructions to determine a trust metric for the third-party model based on a comparison of the probability value differences to a threshold, the trust metric to indicate the third-party model is reliable. 
     
     
         28 . A tangible computer-readable storage medium comprising instructions that, when executed, cause a processor to, at least:
 reduce multi-demographic model iterations by enumerating demographic combinations for households that access at least one of a plurality of features represented in training data, the enumerated demographic combinations including an identifier, the identifier to represent a combination of two or more demographic categories and to include cartesian representations corresponding to occurrences or non-occurrences of a demographic category of interest, the demographic category of interest to be selected from the two or more demographic categories; and   generate probability values for ones of the enumerated demographic combinations based on training coefficients, the probability values to represent likelihoods that the ones of the enumerated demographic combinations accessed the at least one of the plurality of features, the training coefficients corresponding to the ones of the enumerated demographic combinations, the ones of the enumerated demographic combinations associated with (a) a training model and (b) a third-party model.   
     
     
         29 . The computer-readable medium as defined in  claim 28 , wherein the instructions are to cause the processor to identify ones of the households that access the at least one of the features of interest. 
     
     
         30 . The computer-readable medium as defined in  claim 29 , wherein the instructions are to cause the processor to associate demographic combinations with the ones of the households. 
     
     
         31 . The computer-readable medium as defined in  claim 28 , wherein the instructions are to cause the processor to generate the training coefficients based on a multinomial logistic regression with: (a) the training data, and (b) the ones of the enumerated demographic combinations. 
     
     
         32 . The computer-readable medium as defined in  claim 28 , wherein the instructions are to cause the processor to revert the probability values for respective ones of the enumerated demographic combinations to second probability values associated with individual demographic components. 
     
     
         33 . The computer-readable medium as defined in  claim 28 , wherein the instructions are to cause the processor to determine probability value differences between the training model and the third-party model. 
     
     
         34 . The computer-readable medium as defined in  claim 33 , wherein the instructions are to cause the processor to determine a trust metric for the third-party model based on a comparison of the probability value differences to a threshold, the trust metric to indicate the third-party model is reliable. 
     
     
         35 . An apparatus to reduce computational resources for a multi-demographic training model, comprising:
 means for reducing multi-demographic model iterations by enumerating demographic combinations for households that access at least one of a plurality of features represented in training data, the enumerated demographic combinations including an identifier, the identifier to represent a combination of two or more demographic categories and to include cartesian representations corresponding to occurrences or non-occurrences of a demographic category of interest, the demographic category of interest to be selected from the two or more demographic categories; and   means for modeling to generate probability values for ones of the enumerated demographic combinations based on training coefficients, the probability values to represent likelihoods that the ones of the enumerated demographic combinations accessed the at least one of the plurality of features, the training coefficients corresponding to the ones of the enumerated demographic combinations, the ones of the enumerated demographic combinations associated with (a) a training model and (b) a third-party model.   
     
     
         36 . The apparatus as defined in  claim 35 , further including means for aggregating to identify ones of the households that access the at least one of the features of interest. 
     
     
         37 . The apparatus as defined in  claim 36 , wherein the modeling means is to generate the training coefficients based on a multinomial logistic regression with: (a) the training data, and (b) the ones of the enumerated demographic combinations. 
     
     
         38 . The apparatus as defined in  claim 35 , further including means for reverting the probability values for respective ones of the enumerated demographic combinations to second probability values associated with individual demographic components. 
     
     
         39 . The apparatus as defined in  claim 38 , wherein the reverting means is to determine probability value differences between the training model and the third-party model. 
     
     
         40 . The apparatus as defined in  claim 39 , wherein the reverting means is to determine a trust metric for the third-party model based on a comparison of the probability value differences to a threshold, the trust metric to indicate the third-party model is reliable.