INTERPRETABLE MACHINE LEARNING FOR DATA AT SCALE

In systems for interpreting the predictions of a machine learning model with the help of a surrogate model, feature vectors of inputs to the machine learning model can be grouped based on locality sensitive hashes or other hashes that reflect similarity between the feature vectors in matching hash v...

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Bibliographic Details
Main Authors HOLSHEIMER, Kristian, LIU, Fuchen, PARIKH, Jugal, DEMA, Mesfin Adane
Format Patent
LanguageEnglish
French
German
Published 10.04.2024
Subjects
CALCULATING
COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
COMPUTING
COUNTING
PHYSICS
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Summary:In systems for interpreting the predictions of a machine learning model with the help of a surrogate model, feature vectors of inputs to the machine learning model can be grouped based on locality sensitive hashes or other hashes that reflect similarity between the feature vectors in matching hash values. For a given prediction to be interpreted and the corresponding input feature vector, a suitable training dataset for the surrogate model can then be obtained at low computational cost by hashing the input feature vector and retrieving stored feature vectors with matching hash values, along with their respective predictions.
Bibliography:Application Number: EP20220725634