Predictive Modeling System for Automated Resource Allocation
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Solution Overview
Problem
Current approaches to generating predictive models are time-consuming and costly, often exploring only a small portion of the vast predictive modeling space, leading to potentially suboptimal solutions and high expenses, as they rely on ad hoc methods and limited trial-and-error testing.
Innovation Solution
A method that systematically evaluates predictive modeling techniques by determining the suitability of various procedures based on problem characteristics and attributes, allocating resources for execution, and selecting models based on scores, allowing for a more comprehensive exploration of the modeling space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If ad hoc methods and limited trial-and-error testing are used to generate predictive models, then the process is simpler and faster to initiate, but the exploration of the modeling space is incomplete leading to suboptimal solutions
Solution Approach 1:
The system performs preliminary actions by automatically evaluating multiple predictive modeling techniques before final model selection. It pre-processes the evaluation of different modeling approaches, allocating resources systematically to assess their suitability for the specific prediction problem, thereby avoiding the need for extensive manual trial-and-error later.
Solution Approach 2:
The system implements feedback mechanisms by automatically scoring and evaluating predictive models based on their performance. It uses this feedback to iteratively improve model selection, allocating more resources to promising techniques and eliminating underperforming ones, thus systematically improving model accuracy without requiring exhaustive manual testing.
2Manufacturing precision
If a comprehensive evaluation of multiple predictive modeling techniques is performed, then model accuracy improves, but the time and cost of the process increases
Solution Approach 1:
The system changes parameters dynamically by adjusting resource allocation based on the suitability and performance of different predictive modeling techniques. It modifies evaluation depth, computational resources, and testing intensity according to each technique's demonstrated potential, thereby achieving comprehensive evaluation without uniformly high costs and time consumption across all techniques.
Solution Approach 2:
The system applies partial action by evaluating only the most suitable predictive modeling techniques for each specific prediction problem rather than exhaustively testing all possible techniques. It uses automated suitability assessment to identify and focus on the most promising approaches, achieving sufficient model accuracy without the need to explore the entire modeling space.
3Productivity
If automated resource allocation and systematic evaluation are implemented, then the exploration of modeling space improves, but the system complexity increases
Solution Approach 1:
The system implements self-service by automatically evaluating predictive modeling techniques and allocating resources without requiring manual intervention. It autonomously assesses technique suitability, distributes computational resources, and selects optimal models, thereby achieving high evaluation efficiency while the complexity is managed through automation rather than manual processes.
Solution Approach 2:
The system achieves universality by designing a multi-functional automated evaluation framework that can handle diverse predictive modeling techniques and prediction problems through a single unified process. This universal approach manages complexity by providing a standardized methodology applicable across different scenarios rather than requiring separate complex systems for each case.
4Reliability
If extensive trial-and-error testing is conducted to ensure model quality, then model reliability improves, but the cost and time consumption increase significantly
Solution Approach 1:
The system performs preliminary reliability assessment by automatically evaluating and scoring predictive models before deployment. It conducts necessary validation and testing in advance, allocating computational resources to ensure model reliability while avoiding the need for extensive post-deployment trial-and-error that would consume additional energy and time.
Data Source
AI summary
Systems and techniques for predictive data analytics are described. In a method for selecting a predictive model for a prediction problem, the suitabilities of predictive modeling procedures for the prediction problem may be determined based on characteristics of the prediction problem and/or on attributes of the respective modeling procedures. A subset of the predictive modeling procedures may be selected based on the determined suitabilities of the selected modeling procedures for the prediction problem. A resource allocation schedule allocating computational resources for execution of the selected modeling procedures may be generated, based on the determined suitabilities of the selected modeling procedures for the prediction problem. Results of the execution of the selected modeling procedures in accordance with the resource allocation schedule may be obtained. A predictive model for the prediction problem may be selected based on those results.