  PHOTONAI is a high-level Python API designed to simplify and accelerate
machine learning model development. It functions as a unifying framework
allowing the user to easily access and combine algorithms from different
toolboxes into custom algorithm sequences. It is especially designed to support
the iterative model development process and automates the repetitive training,
hyperparameter optimization and evaluation tasks. Importantly, the workflow
ensures unbiased performance estimates while still allowing the user to fully
customize the machine learning analysis. PHOTONAI extends existing solutions
with a novel pipeline implementation supporting more complex data streams,
feature combinations, and algorithm selection. Metrics and results can be
conveniently visualized using the PHOTONAI Explorer and predictive models are
shareable in a standardized format for further external validation or
application. A growing add-on ecosystem allows researchers to offer data
modality specific algorithms to the community and enhance machine learning in
the areas of the life sciences. Its practical utility is demonstrated on an
exemplary medical machine learning problem, achieving a state-of-the-art
solution in few lines of code. Source code is publicly available on Github,
while examples and documentation can be found at www.photon-ai.com.
