Machine Learning Under Test
Explore machine learning testing techniques in this EuroPython conference talk. Delve into the importance of writing high-quality code for machine learning algorithms through automated testing. Examine the unique challenges of testing scientific code, including handling unstable data and avoiding under/overfitting. Learn about specific testing tools like numpy.testing for numerical data. Analyze famous machine learning techniques from a testing perspective, gaining deeper insights into learning model functionality. Suitable for intermediate Python programmers, this practical, code-oriented talk requires no prior knowledge of testing or machine learning algorithms. Cover topics such as linear regression, classification, clustering, supervised learning, unit testing, model performance evaluation, cross-validation, and confusion matrices.