Prediction Interpretation Using Similarity-Based Local Regression
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Solution Overview
Problem
Existing prediction interpretation techniques, such as LIME and those disclosed in Patent Document 1 and Non-Patent Document 2, face a trade-off between high prediction accuracy and interpretation accuracy, leading to low interpretation precision.
Innovation Solution
A prediction interpretation apparatus that includes a data storage unit, a model storage unit, a vicinity user search unit, a linear regression model learning unit, and an interpretation result output unit, which extracts vicinity users based on user similarity and prediction direction to learn a linear regression model for high-precision interpretation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If LIME or conventional interpretation techniques are used to interpret prediction results, then prediction accuracy can be maintained, but interpretation accuracy deteriorates due to counterintuitive or low-precision interpretations
Solution Approach 1:
The invention segments the user population into vicinity users (similar to target user) and non-vicinity users, then applies different interpretation approaches. By focusing the linear regression model only on vicinity users who share similar characteristics and prediction directions, the interpretation becomes more targeted and accurate, resolving the contradiction between maintaining prediction accuracy and improving interpretation validity.
Solution Approach 2:
The invention applies local quality by creating a customized linear regression model specific to each target user's vicinity group rather than using a global interpretation model. This localised approach ensures that interpretation coefficients reflect the actual relationships for similar users, improving interpretation accuracy without sacrificing the overall prediction model's performance.
2Measurement precision
If conventional interpretation methods are applied to entire user data, then comprehensive coverage is achieved, but interpretation precision deteriorates due to heterogeneity among users
Solution Approach 1:
The invention divides the homogeneous user dataset into heterogeneous subgroups based on user similarity and prediction direction. By segmenting users into vicinity groups and applying separate linear regression models to each segment, the system achieves both high interpretation precision for individual users and adaptability to different user characteristics.
Solution Approach 2:
The invention changes the parameters of the regression model by creating user-specific models with different coefficient sets for different vicinity groups. This parameter customization allows the interpretation to adapt to each user's specific context while maintaining high precision through the focused vicinity user approach.
3Measurement precision
If a single global prediction model is used for all users, then model simplicity is maintained, but interpretation accuracy deteriorates due to inability to capture user-specific relationships
Solution Approach 1:
The invention segments the modeling process into two stages: a global prediction model for all users and localized linear regression models for interpretation. This segmentation allows the system to maintain a simple global model for prediction while introducing complexity only where needed for accurate interpretation, thus resolving the contradiction between prediction accuracy and model complexity.
Solution Approach 2:
The invention introduces an intermediary linear regression model that acts as a bridge between the global prediction model and user-specific interpretation needs. This intermediary model captures local relationships without requiring complete redesign of the prediction system, balancing accuracy requirements with complexity constraints.
Data Source
AI summary
A prediction interpretation apparatus, comprising: a data storage unit configured to store data of a plurality of users; a model storage unit configured to store a prediction model learned from data of the whole of the plurality of users; a vicinity user search unit configured to extract vicinity users for the target user from the data storage unit; a linear regression model learning unit configured to learn a linear regression model approximated to the prediction model for the vicinity users; and an interpretation result output unit configured to output an interpretation result of prediction for the target user based on a partial regression coefficient of the linear regression model, wherein the vicinity user search unit extracts the vicinity user by narrowing vicinity user candidates extracted based on distance between users based on a prediction direction of the target user by the prediction model.