Feature Predictive Value Evaluation for Efficient Model Exploration
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Solution Overview
Problem
Conventional predictive modeling approaches are inefficient and costly, often exploring only a small portion of the vast modeling space, and lack effective tools for measuring feature importance across diverse models, leading to suboptimal performance and high resource consumption.
Innovation Solution
A method and apparatus that systematically evaluate the space of potential predictive modeling techniques by performing multiple procedures, determining accuracy scores before and after feature shuffling, and calculating model-specific and model-independent predictive values of features to guide resource allocation and model blending.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional predictive modeling approaches are used, then implementation cost and time are reduced, but model accuracy and comprehensiveness deteriorate because only a small portion of the modeling space is explored
Solution Approach 1:
The system performs preliminary actions by automatically evaluating the predictive value of features before full model development. This includes shuffling feature values to create baseline models, calculating predictive values, and identifying important features in advance, which guides subsequent modeling efforts and improves both efficiency and accuracy
Solution Approach 2:
The system enables self-service through automated feature selection and model evaluation. The computer automatically determines predictive values, ranks features, and guides model development without requiring extensive manual intervention from data analysts, thereby improving productivity while maintaining model quality
2Manufacturing precision
If extensive exploration of the modeling space is performed, then model accuracy improves, but resource consumption and cost increase
Solution Approach 1:
The system extracts and focuses computational resources on the most promising features and modeling approaches. By calculating predictive values and identifying important features, the system extracts the essential elements that contribute to model accuracy, avoiding wasted computation on less important aspects
Solution Approach 2:
The system changes parameters by dynamically adjusting which features are evaluated and how modeling procedures are executed based on predictive value calculations. This allows efficient allocation of computational resources to high-value areas while reducing or eliminating low-value computations
3Measurement precision
If multiple predictive modeling procedures are performed to evaluate diverse models, then feature importance measurement improves, but computational complexity increases
Solution Approach 1:
The system segments the complex task of feature importance measurement into distinct, manageable steps: shuffling feature values, creating baseline models, calculating predictive values, and ranking features. This segmentation makes the overall process more manageable and easier to implement while maintaining measurement precision
Data Source
AI summary
A method for determining the predictive value of a feature may include: (a) performing predictive modeling procedures associated with respective predictive models, wherein performing each modeling procedure includes fitting the associated model to an initial dataset representing an initial prediction problem; (b) determining a first accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the initial prediction problem; (c) shuffling values of a feature across observations included in the initial dataset, thereby generating a modified dataset representing a modified prediction problem; (d) determining a second accuracy score of each of the fitted models, representing an accuracy with which the fitted model predicts an outcome of the modified prediction problem; and (e) determining a model-specific predictive value of the feature for each of the fitted models based on the first and second accuracy scores of the fitted model.