DISTRIBUTED COMPUTATION OF MACHINE LEARNING MODEL PERFORMANCE METRICS

Abstract: techniques are described for determining at least one performance metric of a machine learning model. the techniques including, obtaining a dataset generated by at least using output of the machine learning model, partitioning the dataset into two or more partitions that include one or more elements from the dataset; and generating, for each respective partition, a respective first quantile sketch and a respective second quantile sketch based at least in part on each element in the respective partition. the techniques further including generating a first merged quantile sketch by merging each respective first quantile sketch, generating a second merged quantile sketch by merging each respective second quantile sketch, and determining the at least one performance metric of the machine learning model using the first merged quantile sketch and the second merged quantile sketch.

Inventor(s): Amit Kumar Prajapati, Furqan Abdul Samad Shaikh, Antariksha Bhaduri

CPC Classification: G06N20/00 (Machine learning)

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