Generalized metric for machine learning model evaluation for unsupervised classification

A processing system, comprising:

• a memory comprising computer-executable instructions;
• a processor configured to execute the computer-executable instructions and cause the processing system to:
• for each unsupervised machine learning model of one or more unsupervised machine learning models:
• generate a first set of synthetic inputs for the unsupervised machine learning model of the one or more unsupervised machine learning models;
• provide the first set of synthetic inputs to the unsupervised machine learning model trained to output a prediction for each input of the first set of synthetic inputs, wherein the prediction indicates whether the input is of a first class of a plurality of classes;
• identify, based on an output of the unsupervised machine learning model, a second set of synthetic inputs predicted to be of the first class;
• determine, based on a set of expected normal inputs for the unsupervised machine learning model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model;
• and
• provide the accuracy score for display to a requestor.

A method comprising:

• receiving a plurality of unsupervised machine learning models from a requestor, wherein each unsupervised machine learning model of the plurality of unsupervised machine learning models is trained to output a prediction of whether an input to the unsupervised machine learning model is an anomalous time entry or not an anomalous time entry;
• for each unsupervised machine learning model of the plurality of unsupervised machine learning models:
• generating a first set of synthetic inputs for the unsupervised machine learning model;
• providing the first set of synthetic inputs to the unsupervised machine learning model;
• identifying, based on an output of the unsupervised machine learning model, a second set of synthetic inputs predicted to be not anomalous time entries;
• determining, based on a set of expected normal inputs for the unsupervised machine learning model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model;
• and
• providing the accuracy score for display to the requestor.