| CPC G06F 11/079 (2013.01) | 19 Claims |
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1. A system for detecting a drift in supervised Machine Learning (ML) models and unsupervised ML model, the system comprising:
a processor; and
a memory coupled to the processor, wherein the memory comprises processor-executable instruction, which on execution, cause the processor to:
retrieve current dataset corresponding to an output of one or more supervised ML models and one or more unsupervised ML models;
segregate the current dataset based on a requirement of at least one drift detection model of a plurality of drift detection models;
apply the at least one drift detection model of the plurality of drift detection models to the segregated dataset to generate one or more predictive results corresponding to the current dataset;
calculate a sliding window probability of the current dataset;
track maximum probability values in the current dataset based on the calculated sliding window probability;
determine one or more correct prediction results from the maximum probability values;
detect the drift in the one or more supervised ML models and the one or more unsupervised ML models based on the one or more correct prediction results being below a pre-defined maximum probability value and a pre-defined probability threshold value;
determine one or more errors in the one or more predictive results by comparing the one or more predictive results to one or more reference values associated with the current dataset; and
detect the drift in the one or more supervised ML models and the one or more unsupervised ML models based on the determined one or more errors being greater than a threshold value, wherein the one or more supervised ML models and the one or more unsupervised ML models are corrected based on the detected drift.
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