Generating synthetic data using reject inference processes for modifying lead scoring models

1. In a digital medium environment for classifying lead prospects, a computer-implemented method for using reject inference to generate synthetic data for modify machine-learning lead scoring models comprising:

identifying, by at least one processor, an original dataset corresponding to an output of a machine-learning lead scoring model that generates scores for a plurality of prospects from the original dataset, the scores indicating a likelihood of success of prospects of the plurality of prospects;

determining, by the at least one processor, a success rate threshold by performing a plurality of simulations that utilize the machine-learning lead scoring model with different parameters to determine success rates of historical data associated with the original dataset;

determining, by the at least one processor, that a success rate based on a reject rate and a mislabel rate of the original dataset does not meet the success rate threshold;

selecting, by the at least one processor, a reject inference model from a plurality of reject inference models in response to the success rate not meeting the success rate threshold, each reject inference model of the plurality of reject inference models comprising a model for generating synthetic outcomes for reject data in the original dataset having no outcome data;

generating, by the at least one processor and based on the original dataset, an imputed dataset comprising synthetic outcome data for reject data in a subset of the plurality of prospects to augment the original dataset by using the reject inference model to generate synthetic outcome labels for the reject data in the subset of the plurality of prospects; and

updating, by the at least one processor, the machine-learning lead scoring model using the imputed dataset by modifying at least one parameter of the machine-learning lead scoring model based on synthetic outcome data of the imputed dataset.

2. The computer-implemented method as recited in claim 1 , wherein selecting the reject inference model from the plurality of reject inference models comprises selecting a simple augmentation model for augmenting the original dataset.

3. The computer-implemented method as recited in claim 1 , wherein selecting the reject inference model from the plurality of reject inference models comprises selecting a fuzzy augmentation model for augmenting the original dataset.

4. The computer-implemented method as recited in claim 1 , further comprising:

determining a plurality of characteristics of the original dataset, the plurality of characteristics comprising a split effectiveness of the machine-learning lead scoring model for the original dataset, the success rate of the original dataset, and a size of a set of known labels in the original dataset; and

generating the imputed dataset comprising the synthetic outcome data in response to determining that one or more of the plurality of characteristics do not meet one or more characteristic thresholds.

5. A non-transitory computer readable storage medium comprising instructions that, when executed by at least one processor, cause a computer system to:

identify an original dataset corresponding to an output of a machine-learning lead scoring model that generates scores for a plurality of prospects from the original dataset, the scores indicating a likelihood of success of prospects of the plurality of prospects;

determine a success rate threshold by performing a plurality of simulations that utilize the machine-learning lead scoring model with different parameters to determine success rates of historical data associated with the original dataset;

determine that a success rate based on a reject rate and a mislabel rate of the original dataset does not meet the success rate threshold;

select a reject inference model from a plurality of reject inference models in response to the success rate not meeting the success rate threshold, each reject inference model of the plurality of reject inference models comprising a model for generating synthetic outcomes for reject data in the original dataset having no outcome data;

generate, based on the original dataset, an imputed dataset comprising synthetic outcome data for reject data in a subset of the plurality of prospects to augment the original dataset by using the reject inference model to generate synthetic outcome labels for the reject data in the subset of the plurality of prospects; and

update the machine-learning lead scoring model using the imputed dataset by modifying at least one parameter of the machine-learning lead scoring model based on the synthetic outcome data of the imputed dataset.

6. The non-transitory computer readable storage medium as recited in claim 5 , further comprising instructions that, when executed by the at least one processor, cause the computer system to determine the success rate threshold by performing the plurality of simulations to determine a threshold that meets a specified accuracy with a specified confidence level based on scoring splits for the original dataset.

7. The non-transitory computer readable storage medium as recited in claim 6 , wherein the plurality of reject inference models comprises a simple augmentation model and a fuzzy augmentation model.

8. The non-transitory computer readable storage medium as recited in claim 5 , further comprising instructions that, when executed by the at least one processor, cause the computer system to:

identify a characteristic of the original dataset based on the plurality of prospects in the original dataset;

determine that the characteristic of the original dataset does not meet a characteristic threshold indicating whether to use the original dataset or to generate the synthetic outcome data; and

generate the synthetic outcome data in response to determining that the characteristic of the original dataset does not meet the characteristic threshold.

9. The non-transitory computer readable storage medium as recited in claim 8 , wherein the characteristic comprises a split effectiveness of the machine-learning lead scoring model for the original dataset, the success rate of the original dataset, or a size of a set of known labels in the original dataset.

10. The non-transitory computer readable storage medium as recited in claim 8 , further comprising instructions that, when executed by the at least one processor, cause the computer system to:

compare a plurality of characteristics of the original dataset to a plurality of characteristic thresholds; and

generate the synthetic outcome data in response to determining that the plurality of characteristics of the original dataset do not meet the plurality of characteristic thresholds.

11. The non-transitory computer readable storage medium as recited in claim 10 , further comprising instructions that, when executed by the at least one processor, cause the computer system to determine the plurality of characteristic thresholds based on the plurality of simulations on the historical data associated with the original dataset.

12. The non-transitory computer readable storage medium as recited in claim 5 , further comprising instructions that, when executed by the at least one processor, cause the computer system to determine a plurality of features of the plurality of prospects for generating the synthetic outcome data, wherein determining the plurality of features comprises:

performing a plurality of additional simulations on the historical data associated with the original dataset using variable combinations of the plurality of features; and

selecting a set of features based on a performance of the variable combinations of the plurality of features in the plurality of simulations.

13. The non-transitory computer readable storage medium as recited in claim 5 , further comprising instructions that, when executed by the at least one processor, cause the computer system to score a plurality of new prospects using the updated machine-learning lead scoring model based on the synthetic outcome data.

14. In a digital medium environment for classifying lead prospects, a system for using reject inference to generate synthetic data for modify lead scoring models comprising:

at least one processor; and

a non-transitory computer memory comprising:

an original dataset comprising data for a plurality of prospects; and

instructions that, when executed by the at least one processor, cause the system to:

identify an output of a machine-learning lead scoring model that generates scores for the plurality of prospects from the original dataset, the scores indicating a likelihood of success of each prospect of the plurality of prospects;

determine a success rate threshold by performing a plurality of simulations that utilize the machine-learning lead scoring model with different parameters to determine success rates of historical data associated with the original dataset;

determine that a success rate based on a reject rate and a mislabel rate of the original dataset meets a success rate threshold;

select a reject inference model from a plurality of reject inference models in response to the success rate not meeting the success rate threshold, each reject inference model of the plurality of reject inference models comprising a model for generating synthetic outcomes for reject data in the original dataset having no outcome data;

generate an imputed dataset comprising synthetic outcome data representing simulated outcomes of reject data in a subset of the plurality of prospects to augment the original dataset by using the selected reject inference model on the reject data in the subset of the plurality of prospects; and

modify the machine-learning lead scoring model based on the synthetic outcome data of the imputed dataset by modifying at least one parameter of the machine-learning lead scoring model.

15. The system as recited in claim 14 , further comprising instructions that, when executed by the at least one processor, cause the system to:

identify a plurality of characteristics of the original dataset based on the plurality of prospects in the original dataset;

determine that the plurality of characteristics of the original dataset does not meet a plurality of characteristic thresholds indicating whether to use the original dataset or to generate the synthetic outcome data; and

generate the synthetic outcome data in response to determining that the plurality of characteristics of the original dataset does not meet the plurality of characteristic thresholds.

16. The system as recited in claim 15 , wherein a characteristic of the plurality of characteristics comprises a split effectiveness of the machine-learning lead scoring model for the original dataset, the success rate of the original dataset, or a size of a set of known labels in the original dataset.

17. The system as recited in claim 15 , further comprising instructions that, when executed by the at least one processor, cause the system to determine the plurality of characteristic thresholds based on the plurality of simulations on the historical data associated with the original dataset.

18. The system as recited in claim 14 , wherein the instructions that cause the system to generate the imputed dataset using the selected reject inference model further cause the system to:

identify a plurality of rejected prospects of the plurality of prospects lacking outcome data;

generate, using the selected reject inference model, synthetic outcome data for the plurality of rejected prospects, synthetic outcome data for a rejected prospect comprising a label indicating a predicted successful outcome or a predicted negative outcome; and

generate the imputed dataset by augmenting the original dataset with the synthetic outcome data.

19. The system as recited in claim 14 , further comprising instructions that, when executed by the at least one processor, cause the system to score a plurality of new prospects using the modified machine-learning lead scoring model based on the synthetic outcome data.

20. The system as recited in claim 14 , wherein the plurality of reject inference models comprises a simple augmentation model and a fuzzy augmentation model.