STREAMLINE is an end-to-end automated machine learning (AutoML) pipeline that empowers anyone to easily run, interpret, and apply a rigorous and customizable analysis for data mining or predictive modeling. Notably, this tool is currently limited to supervised learning on tabular, binary classification data but will be expanded as our development continues. The development of this pipeline focused on (1) overall automation, (2) avoiding and detecting sources of bias, (3) optimizing modeling performance, (4) ensuring complete reproducibility (under certain STREAMLINE parameter settings), (5) capturing complex associations in data (e.g. feature interactions), and (6) enhancing interpretability of output. Overall, the goal of this pipeline is to provide a transparent framework to learn from data as well as identify the strengths and weaknesses of ML modeling algorithms or other AutoML algorithms.
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