Systems, apparatus, and methods for generating prediction sets based on a known set of features

1. A method comprising

identifying a plurality of features associated with information of one or more entities;

accessing a probability distribution store comprising a plurality of defined numerical ranges as potential possibilities for being paired with the plurality of features of the one or more entities;

determining a plurality of first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features;

determining a plurality of second probability distributions for each of the defined numeric al ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features;

determining a merged probability distribution using a probabilistic classifier, based on the plurality of first probability distributions and the plurality of second probability distributions;

determining and storing one or more prediction sets based on the merged probability distribution, including generating the one or more prediction sets based on the merged probability distribution by using a Monte Carlo procedure to generate random sampling numbers, wherein the one or more prediction sets comprise a plurality of prediction values corresponding to the one or more entities;

selecting one or more content items to display on a device interface as selected with each entity based on the one or more prediction sets; and

displaying the one or more content items on the device interface.

2. The method of claim 1 , comprising

generating the one or more prediction sets based on the merged probability distribution by using a Naïve-Bayes procedure.

3. The method of claim 1 , wherein the defined numerical ranges comprise a plurality of income level ranges.

4. The method of claim 1 , wherein the determining the plurality of first probability distributions further comprises a prediction that the defined numerical ranges are paired with the plurality of features.

5. The method of claim 1 , wherein the plurality of features comprise one or more registration attributes of the one or more entities and one or more social media content attributes of the one or more entities, and the numerical ranges comprise one or more demographic attributes of the one or more entities.

6. The method of claim 1 , comprising

creating a tag to identify one or more social media content items associated with the one or more entities; and

selecting the one or more content items comprising a similar tag that matches the tag as a potential advertisement to display on the device interface.

7. An apparatus comprising

a processor configured to

identify a plurality of features associated with information of one or more entities;

access a probability distribution store comprising a plurality of defined numerical ranges as potential possibilities for being paired with the plurality of features of the one or more entities;

determine a plurality of first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features;

determine a plurality of second probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features;

determine a merged probability distribution using a probabilistic classifier, based on the plurality of first probability distributions and the plurality of second probability distributions;

determine and store one or more prediction sets based on the merged probability distribution, including generating the one or more prediction sets based on the merged probability distribution by using a Monte Carlo procedure to generate random sampling numbers, wherein the one or more prediction sets comprise a plurality of prediction values corresponding to the one or more entities;

select one or more content items to display on a device interface as selected with each entity based on the one or more prediction sets; and

display the one or more content items on the device interface.

8. The apparatus of claim 7 , wherein the processor is further configured to

generate the one or more prediction sets based on the merged probability distribution by using a Naïve-Bayes procedure.

9. The apparatus of claim 7 , wherein the defined numerical ranges comprise a plurality of income level ranges.

10. The apparatus of claim 7 , wherein the determination that the plurality of first probability distributions further comprises a prediction that the defined numerical ranges are paired with the plurality of features.

11. The apparatus of claim 7 , wherein the plurality of features comprise one or more registration attributes of the one or more entities and one or more social media content attributes of the one or more entities, and the numerical ranges comprise one or more demographic attributes of the one or more entities.

12. The apparatus of claim 7 , wherein the processor is further configured to

create a tag to identify one or more social media content items associated with the one or more entities; and

select the one or more content items comprising a similar tag that matches the tag as a potential advertisement to display on the device interface.

13. A non-transitory computer readable storage medium configured to store instructions that when executed cause a processor to perform:

identifying a plurality of features associated with information of one or more entities;

accessing a probability distribution store comprising a plurality of defined numerical ranges as potential possibilities for being paired with the plurality of features of the one or more entities;

determining a plurality of first probability distributions for each of the defined numerical ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more of the features;

determining a plurality of second probability distributions for each of the defined numeric al ranges indicating probabilities that each defined numerical range is assigned to each entity having one or more additional features;

determining a merged probability distribution using a probabilistic classifier, based on the plurality of first probability distributions and the plurality of second probability distributions;

determining and storing one or more prediction sets based on the merged probability distribution, including generating the one or more prediction sets based on the merged probability distribution by using a Monte Carlo procedure to generate random sampling numbers, wherein the one or more prediction sets comprise a plurality of prediction values corresponding to the one or more entities;

selecting one or more content items to display on a device interface as selected with each entity based on the one or more prediction sets; and

displaying the one or more content items on the device interface.

14. The non-transitory computer readable storage medium of claim 13 , wherein the processor is further configured to perform:

generating the one or more prediction sets based on the merged probability distribution by using a Naïve-Bayes procedure.

15. The non-transitory computer readable storage medium of claim 13 , wherein the defined numerical ranges comprise a plurality of income level ranges.

16. The non-transitory computer readable storage medium of claim 13 , wherein the determining the plurality of first probability distributions further comprises a prediction that the defined numerical ranges are paired with the plurality of features.

17. The non-transitory computer readable storage medium of claim 13 , wherein the plurality of features comprise one or more registration attributes of the one or more entities and one or more social media content attributes of the one or more entities, and the numerical ranges comprise one or more demographic attributes of the one or more entities.