Systems and methods for artificial-intelligence-assisted prediction generation

A computerized confidence interval determination system comprising:

• at least one processor and
• memory coupled to the at least one processor,
• wherein the memory stores:
• a historical data store including training data relating to a particular category and previous prediction requests and
• instructions for execution by the at least one processor and
• wherein the instructions include, in response to receiving a prediction request from a user device:
• determining a size of each element of the prediction request;
• obtaining a previous prediction request from the historical data store;
• determining whether a match exists between elements of the prediction request and elements of the previous prediction request, including comparing each element of the prediction request to a respective element of the previous prediction request;
• in response to a determination that a match exists, determining whether the training data has changed since the previous prediction request was made;
• in response to a determination that the training data has not changed:
• obtaining a previously stored classifier, and
• using the previously stored classifier to determine a set of predicted likelihoods;
• in response to any mismatch between the elements of the prediction request and the elements of the previous prediction request, for each element of the prediction request:
• determining a set of bins that correspond to the element based on training data,
• generating an updated classifier based on the training data, and
• for each bin of the set of bins of the element, determining the set of predicted likelihoods using the updated classifier;
• in response to a determination that the training data has changed, for each element of the prediction request:
• determining the set of bins that correspond to the element based on training data,
• generating the updated classifier based on the training data, and
• for each bin of the set of bins of the element, determining the set of predicted likelihoods using the updated classifier;
• generating a mean vector prediction and a covariance matrix using the set of bins generated for each element of the prediction request;
• determining a predicted outcome and a confidence interval using the mean vector prediction and the covariance matrix;
• and
• in response to a determination that the confidence interval exceeds a threshold, outputting, to the user device, the predicted outcome and the confidence interval.