Global Feature Importance Explanations via Adaptive Sampling
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Solution Overview
Problem
Interpreting complex machine learning models, particularly deep learning models, is challenging due to their increased complexity and reduced interpretability, which hinders understanding of their predictions and decision-making processes.
Innovation Solution
Adaptive sampling and fast clustering-sampling methods are employed to down-sample large tabular datasets, generating a representative subset for explaining machine learning models, using model explainers to maintain explanation quality and improve speed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep learning models with tens to hundreds of layers are used to solve complex problems, then model capacity and performance are improved, but interpretability and understanding of predictions are reduced
Solution Approach 1:
The patent introduces an intermediary explanation system that acts as a mediator between the complex deep learning model and human users. This explanation system generates human-understandable descriptions of model predictions, allowing the complex model to maintain its high capacity while providing interpretable outputs through the intermediary explanation layer.
Solution Approach 2:
The patent extracts the interpretability function from the complex model by separating it into a distinct explanation generation component. This allows the core predictive model to remain complex and high-capacity while the extracted explanation component handles the interpretability task independently.
2Measurement precision
If explanation quality is maintained by analyzing full datasets, then interpretation accuracy is improved, but computation time and resource usage increase
Solution Approach 1:
The patent applies partial action by analyzing only a carefully selected subset of the full dataset rather than the entire dataset. This subset is chosen to be representative and sufficient for generating accurate explanations, thereby reducing computation time while maintaining explanation quality through strategic sampling rather than exhaustive analysis.
Solution Approach 2:
The patent performs preliminary actions by pre-processing and selecting representative samples before generating explanations. This preliminary sampling and filtering step prepares a condensed dataset that retains the essential information needed for accurate explanations, reducing the computational burden of subsequent analysis.
Data Source
AI summary
End-to-end explanation techniques, which efficiently explain the behavior (feature importance) of any machine learning model on large tabular datasets, are disclosed. These techniques comprise two down-sampling methods to efficiently select a small set of representative samples of a high-dimensional dataset for explaining a machine learning model by making use of the characteristics of the dataset or of an explainer of a machine learning model to optimize the explanation quality. These techniques significantly improve the explanation speed while maintaining the explanation quality of a full dataset evaluation.