Analytics Model Feature Hierarchy for Provenance Accuracy Tradeoffs
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Solution Overview
Problem
Current data modeling techniques fail to provide a clear understanding of the features used to shape analytics models, leading to valuable insights being overlooked due to complexity, especially in fields like healthcare where data provenance is crucial.
Innovation Solution
A method is introduced to generate analytics models by defining a second set of features based on domain knowledge, creating a feature hierarchy, and selecting features that maximize provenance while maintaining a defined accuracy threshold, allowing for more meaningful and understandable models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If domain knowledge is applied to create high-level features, then model comprehensibility and provenance are improved, but model accuracy may deteriorate
Solution Approach 1:
The system dynamically adjusts the composition of features in the analytics model by evaluating multiple candidate models with different feature combinations. It selects the model that optimizes the balance between comprehensibility (provenance) and accuracy based on defined criteria, allowing the model to adapt between these two competing objectives rather than fixing on one approach.
Solution Approach 2:
The system changes the parameters of the analytics model by incorporating high-level features derived from domain knowledge while maintaining a controlled set of low-level features. This parameter adjustment allows the model to achieve better comprehensibility without completely sacrificing accuracy, as the system evaluates multiple configurations and selects the optimal balance.
2Quantity of substance
If more features are incorporated into the analytics model, then model comprehensiveness is improved, but model complexity increases
Solution Approach 1:
The system segments features into distinct hierarchical levels (high-level features derived from domain knowledge and low-level features from raw data). This segmentation allows the model to incorporate comprehensive features while organizing them in a structured hierarchy that reduces apparent complexity and improves interpretability.
Solution Approach 2:
The system adds a hierarchical dimension to the feature structure by organizing features into multiple levels. This dimensional change allows the model to accommodate a larger number of features without proportionally increasing complexity, as the hierarchical organization provides a framework for understanding and managing the features.
3Loss of information
If high-level features with domain knowledge are used, then provenance is improved, but prediction accuracy may worsen
Solution Approach 1:
The system creates a composite feature structure by combining high-level features (which provide provenance and interpretability) with low-level features (which provide predictive power). This composite approach allows the model to benefit from both the domain knowledge embedded in high-level features and the detailed information in low-level features, achieving a balance between provenance and accuracy.
Solution Approach 2:
The system uses low-level features as intermediaries that connect raw data to high-level domain knowledge features. This intermediary layer allows the model to maintain the predictive accuracy benefits of detailed data while incorporating the provenance benefits of high-level features, as the intermediaries bridge the gap between the two types of features.
Data Source
AI summary
Generating a data model may include receiving a raw data set and generating a first repository based on a first set of features of the raw data set, a second repository having a second set of features based on an aggregation of features of the first repository, and a third repository having a third set of features based on the first and second features sets. The data model may be generated based on a tradeoff between accuracy and provenance of the model.