Meta-data centric approach for predicted class reassignment

A system, comprising:

• a memory storing instructions;
• and
• at least one processor communicatively connected to the memory and configured to execute the instructions to:
• obtain performance metrics of instances of classifications performed by a classification model in classifying data samples, wherein the classification model is trained to classify the data samples as belonging to a class;
• generate a confusion matrix for the classification model, wherein the confusion matrix comprises the instances clustered into a plurality of groups, the plurality of groups comprising a true positive group, a false positive group, a true negative group, and a false negative group;
• for each group of the plurality of groups, derive a threshold from the performance metrics for instances of data samples constituting the respective group;
• predict a classification for an input data sample by applying the input data sample to the classification model;
• predict a classification for an input data sample by applying the input data sample to the classification model;
• and
• update the predicted classification based on determining whether or not the predicted classification is correct or not.

A non-transitory computer-readable storage medium storing instructions that, when executed by a processor, cause the processor to:

• extract metadata associated with instances of classifying data by a machine learning model trained to classify data as belonging to a class, the metadata comprising confidence score and a tag indicative of a correctness for each instance;
• cluster the instances into a plurality of groups based on the tags;
• derive a plurality of thresholds for each group of the plurality of groups based on confidence scores associated with instances clustered into a respective group;
• evaluate a predicted classification, by the machine learning model, for an input data sample based on a comparison of confidence score for the predicted classification to one or more of the plurality of thresholds;
• and
• generate a label for the input data sample based on the evaluation, wherein the label is indicative of whether or not the input data sample belongs to the class.