Classification Model Interpretability via Feature Contribution Segmentation
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Solution Overview
Problem
Existing systems struggle to improve the interpretability of classification model output, which hinders decision-making processes due to the opacity of the model and the multitude of factors involved in data approval evaluations.
Innovation Solution
The system determines the contribution of each feature of the input data to the classification model output and presents these contributions along with ratios of similar historical data instances, enhancing the interpretability of the model's predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a classification model is used to predict approval outcomes, then prediction accuracy is improved, but model interpretability deteriorates due to opacity
Solution Approach 1:
The patent segments the model's decision-making process by identifying and presenting individual feature contributions separately. Each feature's impact on the prediction is broken down and displayed independently, allowing users to understand how each input variable influences the output without sacrificing the model's predictive accuracy.
Solution Approach 2:
The patent introduces an intermediary layer between the black-box model and the user by presenting feature contribution information. This intermediary presentation layer translates the model's internal decision logic into human-understandable feature importance metrics, bridging the gap between accurate predictions and interpretability.
2Ease of operation
If predicted probability is provided to assist approvers, then decision-making support is improved, but approver confusion increases due to opacity of the model
Solution Approach 1:
The patent segments the prediction information into discrete feature contributions rather than presenting a single opaque probability. By breaking down the prediction into individual feature impacts, approvers can understand which specific factors are driving the prediction, reducing confusion while maintaining decision-making support.
Solution Approach 2:
The patent uses visual indicators (such as colored bars or markers) to represent feature contributions, where different colors or intensities indicate the direction and magnitude of each feature's impact. This visual encoding makes the model's reasoning transparent and easier for approvers to interpret without overwhelming them with complex information.
3Adaptability or versatility
If multiple factors are considered in data approval evaluation, then evaluation comprehensiveness is improved, but interpretability of the prediction deteriorates
Solution Approach 1:
The patent handles multiple evaluation factors by segmenting them into individual feature contributions. Each factor is presented separately with its specific impact on the prediction, allowing comprehensiveness to be maintained while interpretability is preserved through clear, discrete presentation of each factor's role.
Solution Approach 2:
The patent adds a new dimension to the prediction output by including feature contribution information alongside the prediction probability. This additional dimension of explanation transforms the multi-factor evaluation from an opaque complex process into a transparent, multi-dimensional output that includes both the prediction and the reasoning behind it.
Data Source
AI summary
Systems include reception of input data comprising a value for each of a plurality of features, input of the input data to a trained model to determine a label and a confidence level, determination of a contribution of each feature to the label based on the model and the label, determination of a set of features based on the determined contributions, determination of labeled data instances, comprising a value for each of the plurality of features and a fixed label, determination, for each feature of the set, of a ratio of a number of the labeled data instances having the same value as the input data and the determined label to a number of labeled data instances having the same value as the input data and not the determined label, presentation of the determined label and the confidence level, and presentation of each feature and its determined ratio.