This post represents views on why Machine Learning systems or models are termed as non-testable from quality control/quality assurance perspectives. Before I proceed, let me humbly state that data scientists and the Machine Learning community have been saying that ML models are testable as they are first trained and then tested using techniques such as cross-validation etc. based on different techniques to increase the model performance and optimize the model. However, "testing" the model is referred with the scenario during the development (model building) phase when data scientists test the model performance by comparing the model outputs (predicted values) with the actual values. This is not the same as testing the model for any given input for which the output (expected) value is not known beforehand. In this post, I am rather talking about ML models testability from the overall traditional software testing perspective.

Given that Machine Learning systems are non-testable, it can be said that performing QA or quality control checks on Machine Learning systems is not easy, and, thus, a matter of concern given the trust, the end-users need to have on such systems. Project stakeholders must need to understand the non-testability aspects of Machine Learning systems in order to put appropriate quality controls in place to serve trustable Machine Learning models to end users in production. This applies greatly to healthcare and financial systems where a couple of false negatives or type-II error could lead to havoc or troubles for the stakeholders.

Source de l’article sur DZONE (AI)