A novel approach for introducing basic elementary disturbances for evaluating machine learning models

This study looks at the testing of models for computer training. The issue with the existing research methods is that testing is mostly case-specific and requires considerable extra work. A new approach is used to introduce basic elementary interference to the input data. The approach is commonly used to work with many types of data and machine learning models. Simple disruptions can be used to forecast the handling of unexposed disturbances by a learning model. An overall test method can be helpful as a clear predictor of the tolerance of the model to intangible disorders.