The present disclosure includes methods and systems for generating digital predictive models by progressively sampling a repository of data samples. In particular, one or more embodiments of the disclosed systems and methods identify initial attributes for predicting a target attribute and utilize the initial attributes to identify a coarse sample set. Moreover, the disclosed systems and methods can utilize the coarse sample set to identify focused attributes pertinent to predicting the target attribute. Utilizing the focused attributes, the disclosed systems and methods can identify refined data samples and utilize the refined data samples to identify final attributes and generate a digital predictive model.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. In a digital medium environment, a computer-implemented method of selecting attributes for generating digital models, comprising: determining initial attributes for building a digital predictive model to predict a target attribute; a step for identifying final attributes from the initial attributes to utilize in generating the digital predictive model; and generating the digital predictive model for predicting the target attribute based on the final attributes. 2. The method of claim 1, wherein the number of initial attributes is greater than the number of final attributes. 3. The method of claim 1 wherein the final attributes are more significant in predicting the target attribute than one or more of the initial attributes. 4. The method of claim 1, further comprising identifying a final sample set from a repository of data samples wherein each data sample in the final sample set comprises the final attributes. 5. The method of claim 1, wherein generating the digital predictive model for predicting the target attribute based on the final attributes comprises generating the digital predictive model based on the final sample set. 6. The method of claim 1, further comprising: determining that the number of initial attributes exceed an attribute threshold; and performing the step for identifying the final attributes based on the determination that the number of initial attributes exceed the attribute threshold. 7. The method of claim 1, further comprising: detecting one or more input attributes in relation to a user; and utilizing the digital predictive model to predict the target attribute in relation to the user based on the one or more detected input attributes and the final attributes. 8. In a digital medium environment, a computer-implemented method of selecting attributes for generating digital predictive models, comprising: determining initial attributes for building a digital predictive model to predict a target attribute; identifying a coarse sample set from a repository of data samples by querying the repository of data samples for a coarse number of data samples, wherein each of the data samples in the coarse sample set comprise the initial attributes; selecting a focused subset of the initial attributes based on the coarse sample set; identifying a refined sample set from the repository of data samples by querying the repository of data samples for a refined number of data samples, wherein the refined number of data samples is larger than the coarse number of data samples, and wherein each of the data samples in the refined sample set comprises the focused subset of the initial attributes; and generating the digital predictive model for predicting the target attribute based on the refined sample set. 9. The method of claim 8, wherein selecting the focused subset of the initial attributes comprises: for each data sample in the coarse sample set, comparing the initial attributes from the data sample to the target attribute from the data sample; and identifying the focused subset of the initial attributes based on the comparison. 10. The method of claim 8, further comprising determining the coarse number of data samples based on the number of initial attributes. 11. The method of claim 10, further comprising determining the coarse number of data samples based on the refined number of data samples. 12. The method of claim 8, wherein generating the digital predictive model based on the refined sample set comprises: selecting final attributes from the focused subset of the initial attributes based on the refined sample set; and generating the digital predictive model based on the final attribute set. 13. The method of claim 8, wherein generating the digital predictive model based on the final attribute set further comprises: identifying a final sample set wherein each of the data samples in the final sample set comprises the final attributes; and utilizing the final sample set to generate the digital predictive model. 14. The method of claim 13, wherein identifying the final sample set comprises filtering the refined sample set utilizing the final attributes to remove one or more attributes from the focused subset of the initial attributes. 15. The method of claim 1, further comprising: detecting one or more input attributes in relation to a user; and utilizing the digital predictive model to predict the target attribute in relation to the user based on the one or more detected input attributes. 16. A system for improving digital attribute selection in generating digital predictive models, comprising: one or more memories comprising a repository of data samples, wherein the repository of data samples reflects a plurality of samples and a corresponding set of attributes for each sample; at least one computing device storing instructions thereon, that, when executed by at least one computing device, cause the system to: identify a coarse sample set from the repository of data samples by querying the repository of data samples for a coarse number of data samples, wherein each of the data samples in the coarse sample set comprise initial attributes from the set of attributes; select a focused subset of the initial attributes based on the coarse sample set; determine final attributes by performing acts comprising: identifying a refined sample set from the repository of data samples by querying the repository of data samples for a refined number of data samples, wherein the refined number of data samples is larger than the coarse number of data samples, and wherein each of the data samples in the refined sample set comprise the focused subset of the initial attributes; and selecting the final attributes based on the refined sample set; and generate a digital predictive model based on the final attributes. 17. The system of claim 16, further comprising instructions that, when executed by the at least one computing device, cause the system to select the focused subset of the initial attributes by: for each data sample in the coarse sample set, comparing the initial attributes from the data sample to the target attribute from the data sample; and identifying the focused subset of the initial attributes based on the comparison. 18. The system of claim 16, further comprising instructions that, when executed by the at least one computing device, cause the system to determine the coarse number of data samples based on the number of initial attributes. 19. The system of claim 16, further comprising instructions that, when executed by the at least one computing device, cause the system to: determine that the number of initial attributes exceed an attribute threshold; and identify the coarse sample set and select the focused subset of the initial attributes based on the determination that the number of initial attributes exceed the attribute threshold. 20. The system of claim 16, further comprising instructions that, when executed by the at least on computing device, cause the system to generate the digital predictive model based on the final attributes by: identifying a final sample set wherein each data sample in the final sample set comprises the final attributes; and utilizing the final sample set to generate the digital predictive model.