Regression Model Recommendation Engine for Target Outcome Adjustment

The patent introduces an intermediary system that includes a data generator, simulation engine, and recommendation generator. This intermediary takes the regression model's predictions and augments them with additional information about attribute modifications needed to achieve target outcomes. The intermediary processes the relationship between input attributes and predicted outcomes, providing users with actionable insights about which attributes to change and by how much, without altering the core regression model's prediction accuracy.

The patent segments the complex problem of achieving target outcomes into distinct functional components: a data generator that creates training datasets, a simulation engine that applies the regression model to modified attributes, and a recommendation generator that analyzes results and provides guidance. This segmentation allows each component to handle a specific aspect of the problem, reducing the perceived complexity for users while maintaining the ability to achieve precise target outcomes through iterative attribute modification.

The patent implements a strategy where the simulation engine generates multiple modified training records with varying degrees of attribute changes, but the recommendation generator evaluates these records in stages or prioritizes them based on expected impact. Rather than exhaustively evaluating all possible attribute modifications, the system performs partial evaluation on the most promising candidates first, achieving sufficient recommendation accuracy without requiring exhaustive computational analysis of every possible modification scenario.