Methods and apparatus to generate computer-trained machine learning models to correct computer-generated errors in audience data

Abstract

Methods, apparatus, systems and articles of manufacture are disclosed to generate computer-trained machine learning models to correct computer-generated errors in audience data. An example apparatus includes a query selector to select a plurality of features and a range of hyperparameters; a query generator to generate a plurality of machine learning models based on the plurality of features and the range of hyperparameters, and initiate training of the plurality of machine learning models based on demographic data in a privacy-protected cloud environment, the demographic data obtained from database proprietor user accounts corresponding to audience measurement panelists; and a model selector to select a first machine learning model from the plurality of machine learning models.

1 . An apparatus comprising:
 a query selector to select a plurality of features and a range of hyperparameters;   a query generator to:
 generate a plurality of machine learning models based on the plurality of features and the range of hyperparameters; and 
 initiate training of the plurality of machine learning models based on demographic data in a privacy-protected cloud environment, the demographic data obtained from database proprietor user accounts corresponding to audience measurement panelists; and 
   a model selector to select a first machine learning model from the plurality of machine learning models.   
     
     
         2 . The apparatus of  claim 1 , wherein the query generator is to initiate the training of the plurality of machine learning models in parallel. 
     
     
         3 . The apparatus of  claim 1 , further including an analytics controller to generate performance results for the plurality of machine learning models. 
     
     
         4 . The apparatus of  claim 3 , wherein the analytics controller is to:
 compare results from training the plurality of machine learning models to at least some of the demographic data of ones of the audience measurement panelists who access media via panelist client devices; and   generate the performance results based on the comparison.   
     
     
         5 . The apparatus of  claim 4 , wherein the performance results include at least one of model accuracy or demographic accuracy. 
     
     
         6 . The apparatus of  claim 4 , further including a data aggregation controller to aggregate the performance results of the plurality of machine learning models. 
     
     
         7 . The apparatus of  claim 6 , wherein the model selector is to select the first machine learning model from the plurality of machine learning models based on the aggregated performance results. 
     
     
         8 . The apparatus of  claim 1 , wherein the privacy-protected cloud environment includes first data from at least one of media providers or third parties combined with second data from a database proprietor in a data store, the second data including the demographic data. 
     
     
         9 . A non-transitory computer readable storage medium comprising instructions that, when executed, cause at least one processor to:
 select a plurality of features and a range of hyperparameters;   generate a plurality of machine learning models based on the plurality of features and the range of hyperparameters;   initiate training of the plurality of machine learning models based on demographic data in a privacy-protected cloud environment, the demographic data obtained from database proprietor user accounts corresponding to audience measurement panelists; and   select a first machine learning model from the plurality of machine learning models.   
     
     
         10 . The non-transitory computer readable storage medium of  claim 9 , wherein the instructions, when executed, cause the at least one processor to initiate the training of the plurality of machine learning models in parallel. 
     
     
         11 . The non-transitory computer readable storage medium of  claim 9 , wherein the instructions, when executed, cause the at least one processor to generate performance results for the plurality of machine learning models. 
     
     
         12 . The non-transitory computer readable storage medium of  claim 11 , wherein the instructions, when executed, cause the at least one processor to:
 compare results from training the plurality of machine learning models to at least some of the demographic data of ones of the audience measurement panelists who access media via panelist client devices; and   generate the performance results based on the comparison.   
     
     
         13 . (canceled) 
     
     
         14 . The non-transitory computer readable storage medium of  claim 12 , wherein the instructions, when executed, cause the at least one processor to aggregate the performance results of the plurality of machine learning models. 
     
     
         15 . The non-transitory computer readable storage medium of  claim 14 , wherein the instructions, when executed, cause the at least one processor to select the first machine learning model from the plurality of machine learning models based on the aggregated performance results. 
     
     
         16 - 24 . (canceled) 
     
     
         25 . An apparatus comprising:
 memory; and   at least one processor to execute computer readable instructions to at least:
 select a plurality of features and a range of hyperparameters; 
 generate a plurality of machine learning models based on the plurality of features and the range of hyperparameters; 
 initiate training of the plurality of machine learning models based on demographic data in a privacy-protected cloud environment, the demographic data obtained from database proprietor user accounts corresponding to audience measurement panelists; and 
 select a first machine learning model from the plurality of machine learning models. 
   
     
     
         26 . The apparatus of  claim 25 , wherein the at least one processor is to execute the computer readable instructions to initiate the training of the plurality of machine learning models in parallel. 
     
     
         27 . The apparatus of  claim 25 , wherein the at least one processor is to execute the computer readable instructions to generate performance results for the plurality of machine learning models. 
     
     
         28 . The apparatus of  claim 27 , wherein the at least one processor is to execute the computer readable instructions to:
 compare results from training the plurality of machine learning models to at least some of the demographic data of ones of the audience measurement panelists who access media via panelist client devices; and   generate the performance results based on the comparison.   
     
     
         29 . (canceled) 
     
     
         30 . The apparatus of  claim 28 , wherein the at least one processor is to execute the computer readable instructions to aggregate the performance results of the plurality of machine learning models. 
     
     
         31 . The apparatus of  claim 30 , wherein the at least one processor is to execute the computer readable instructions to select the first machine learning model from the plurality of machine learning models based on the aggregated performance results. 
     
     
         32 . (canceled)