Continuous Variable Binning for Sensitive-Range Interpretability
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Solution Overview
Problem
Existing post-modeling analysis techniques generate lower quality interpretability data for machine learning models, particularly for continuous variables with sensitive ranges, leading to underestimation or overestimation of importance, scaling issues, and lack of explainability regarding sensitivity.
Innovation Solution
A method that generates bins for continuous variables, calculates overall sensitivity values for neighbor bins, and merges those with sensitivity values below a threshold to create improved interpretability data, considering target variance, range correlation, and set correlation values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing post-modeling analysis techniques are used to generate interpretability data, then the analysis process is simple, but the quality of interpretability data is low and sensitivity ranges are not accurately captured
Solution Approach 1:
The patent segments continuous variables into bins and further divides them into sensitive ranges based on sensitivity calculations. This segmentation allows the system to identify and highlight specific ranges where the model is most sensitive, improving interpretability quality without requiring complete redesign of the analysis process
Solution Approach 2:
The patent performs preliminary calculations of sensitivity values for all bins before generating the final interpretability data. By pre-calculating sensitivity metrics and identifying sensitive ranges in advance, the system improves data quality while maintaining a structured and manageable analysis workflow
2Loss of information
If continuous variables are analyzed without considering sensitive ranges, then the analysis process is straightforward, but important sensitivity information is lost leading to underestimation or overestimation of importance
Solution Approach 1:
The patent applies local quality by calculating and reporting sensitivity values specifically for bins that fall within sensitive ranges, rather than treating all bins uniformly. This allows the system to preserve and highlight critical sensitivity information while maintaining a relatively simple overall processing approach
Solution Approach 2:
By segmenting the continuous variable range into discrete bins and then identifying which bins constitute sensitive ranges, the system preserves sensitivity information that would otherwise be lost in continuous analysis, without requiring complex transformations of the entire variable distribution
3Measurement precision
If all bins are treated equally in the analysis, then the processing is simple, but scaling issues arise and interpretability is reduced
Solution Approach 1:
The patent implements local quality by differentiating the treatment of bins based on their sensitivity characteristics. Bins within sensitive ranges receive specialized processing and highlighting, while other bins are treated more generally, improving interpretability without requiring complete redesign of the processing pipeline
Solution Approach 2:
The system dynamically identifies and adapts to sensitive ranges within the data distribution. By calculating sensitivity values and identifying critical ranges during analysis, the system automatically adjusts its interpretability output to highlight important areas, improving measurement precision without requiring manual configuration
Data Source
AI summary
An embodiment for generating interpretability data for a target machine learning model by determining sensitive ranges of continuous variables for post-modeling analysis. The embodiment may receive, for the target machine learning model, historical data including a series of relevant continuous variables. The embodiment may generate bins for each relevant continuous variable in the series of relevant continuous variables. The embodiment may calculate overall sensitivity values for pairs of neighbor bins. The embodiment may, in response to the calculated sensitivity value of respective pairs of neighbor bins being below a predetermined threshold value, merge the respective pairs of neighbor bins. The embodiment may generate interpretability data for the target machine learning model based on the merged respective pairs of neighbor bins.