Efficient and scalable computation of global feature importance explanations
US12380357B2 · kind B2 · utility
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Key dates
| Filing date | Nov 30, 2020 |
| Grant date | Aug 5, 2025 |
| Priority date | — |
| Expiry date | Jan 26, 2044 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N7/01
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
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.
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