Standardized Evaluation of Machine Learning Methods for Evolving Data Streams
This work addresses the problem of inconsistent and unreliable evaluation in online machine learning for researchers and practitioners, though it is incremental as it builds on existing libraries and concepts.
The paper tackles the lack of standardized evaluation for online machine learning methods in evolving data streams by proposing a comprehensive set of properties, performance measures, and evaluation strategies, and introduces an open-source Python framework called float to facilitate more reliable testing and comparison.
Due to the unspecified and dynamic nature of data streams, online machine learning requires powerful and flexible solutions. However, evaluating online machine learning methods under realistic conditions is difficult. Existing work therefore often draws on different heuristics and simulations that do not necessarily produce meaningful and reliable results. Indeed, in the absence of common evaluation standards, it often remains unclear how online learning methods will perform in practice or in comparison to similar work. In this paper, we propose a comprehensive set of properties for high-quality machine learning in evolving data streams. In particular, we discuss sensible performance measures and evaluation strategies for online predictive modelling, online feature selection and concept drift detection. As one of the first works, we also look at the interpretability of online learning methods. The proposed evaluation standards are provided in a new Python framework called float. Float is completely modular and allows the simultaneous integration of common libraries, such as scikit-multiflow or river, with custom code. Float is open-sourced and can be accessed at https://github.com/haugjo/float. In this sense, we hope that our work will contribute to more standardized, reliable and realistic testing and comparison of online machine learning methods.