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Due to the unspecified and dynamic nature of data streams, online machinelearning requires powerful and flexible solutions. However, evaluating onlinemachine learning methods under realistic conditions is difficult. Existing worktherefore often draws on different heuristics and simulations that do notnecessarily produce meaningful and reliable results. Indeed, in the absence ofcommon evaluation standards, it often remains unclear how online learningmethods will perform in practice or in comparison to similar work. In thispaper, we propose a comprehensive set of properties for high-quality machinelearning in evolving data streams. In particular, we discuss sensibleperformance measures and evaluation strategies for online predictive modelling,online feature selection and concept drift detection. As one of the firstworks, we also look at the interpretability of online learning methods. Theproposed evaluation standards are provided in a new Python framework calledfloat. Float is completely modular and allows the simultaneous integration ofcommon libraries, such as scikit-multiflow or river, with custom code. Float isopen-sourced and can be accessed at https://github.com/haugjo/float. In thissense, we hope that our work will contribute to more standardized, reliable andrealistic testing and comparison of online machine learning methods.

Standardized Evaluation of Machine Learning Methods for Evolving Data Streams