Abstract: The computing device generates a classification model providing prediction data indicating predicted users in a target population who will respond to a target stimulus according to a predefined user response category. The computing device displays in GUI a graphical representation of a generated classification model and a plurality of options each specifying one of different objectives for determining a proportion of users in the target population to expose to the target stimulus. The computing device predicts proportion data indicating the proportion of users in the target population to expose to the target stimulus based on the determined location of the cut-off. The computing device issues one or more indications as to whether to use the classification model as a basis for exposing the proportion of users in the target population to the target stimulus according to the proportion data.

Abstract: One exemplary system can determine datasets to be transmitted to client devices for eliciting performance of a computing-task. The datasets include a first dataset and a second dataset. The system can then generate sets of data-curves for each of the datasets based on test data. The system can also generate a new client-device (NCD) projection based on the test data. The system can generate a new client-device task-completion (NCDTC) projection using (i) the NCD projection, and (ii) a particular set of data-curves for the first dataset. The system can determine a first value based at least in part on the NCDTC projection. The system can also determine a second value via series of steps. The system can determine an impact value by subtracting the second value from the first value. The system may allocate computing resources based on the first value, the second value, or the impact value.