GENERALIZED METRIC FOR MACHINE LEARNING MODEL EVALUATION FOR UNSUPERVISED CLASSIFICATION

摘要

Certain aspects of the present disclosure provide techniques for generalized metric for machine learning model evaluation for unsupervised classification including: for each unsupervised machine learning model of one or more unsupervised machine learning models: generating a first set of synthetic inputs for the model of the one or more unsupervised machine learning models; providing the first set of synthetic inputs to the 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; identifying, based on an output of the model, a second set of synthetic inputs predicted to be of the first class; determining, based on a set of expected normal inputs for the model and the second set of synthetic inputs, an accuracy score for the unsupervised machine learning model; and providing the accuracy score for display.