Model Optimization System for Variable Scoring and Data Quality
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Solution Overview
Problem
Existing modeling techniques face challenges in accurately evaluating and improving the quality of input data for predicting sales, as poor data quality often leads to inaccurate models, requiring extensive manual analysis and being costly to rectify.
Innovation Solution
A model optimization system that determines the quality of input variables by using quality metrics and weights to generate scores, allowing for the identification and modification of insufficient variables to improve model accuracy, thereby optimizing data sufficiency for accurate predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual analysis of input parameters is performed to identify inaccuracies, then model accuracy can be improved, but time and labor requirements increase significantly
Solution Approach 1:
The system automatically evaluates data quality and identifies problematic input parameters without requiring manual analysis. The quality assessment module self-evaluates data sufficiency and consistency, generating recommendations for improvement automatically, thereby eliminating the need for time-consuming manual review while maintaining model accuracy.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with an automated computer-based quality assessment system. The system uses algorithms to automatically evaluate data quality metrics, identify problematic parameters, and generate improvement recommendations, substituting human analytical effort with automated computational processes.
2Reliability
If data collection and model building costs are incurred, then model accuracy can be improved, but resources are wasted when data quality is insufficient
Solution Approach 1:
The system performs preliminary quality assessment of input data before model building begins. The quality assessment module evaluates data sufficiency and consistency in advance, identifying problematic parameters beforehand. This allows organizations to correct data quality issues prior to model construction, preventing wasted resources on inaccurate models while maintaining the ability to build accurate models when data quality is sufficient.
3Measurement precision
If quality metrics are evaluated for each input variable, then data quality can be assessed comprehensively, but system complexity increases
Solution Approach 1:
The system segments the quality assessment process into distinct modular components: data collection, quality metric evaluation, scoring calculation, and recommendation generation. Each module handles a specific aspect of quality assessment independently, making the overall system more manageable and easier to implement while maintaining comprehensive evaluation capabilities across all input variables.
Data Source
AI summary
A model optimization system is configured to determine quality of variables for model generation. A data storage stores input variables, quality metrics for the input variables, and weights for the quality metrics. The quality metrics describe sufficiency of data for the input variables and the data is provided for a plurality of regions. A scoring module determines a score for each region based on the input variables and the weighted quality metrics. An optimizer determines whether at least one of the input variables for a region is to be modified based on the scores, and determines whether the total score for the region is operable to be improved using a modified input variable.