PCS workflow, interpretable machine learning, and DeepTune

Bin Yu
University of California, Berkeley (UC Berkeley)
Department of Statistics

In this talk, I'd like to discuss the intertwining importance and connections
of three principles of data science: predictability, computability
and stability (PCS) and the PCS workflow that is built on the three principles.
I will also define interpretable machine learning
through the PDR desiderata (Dredictive accuracy, Descrptive accuracy and Relevancy) and discuss stability as a minimum requirement for interpretability.

The principles will be demonstrated in the context of one collaborative DeepTune project
in neuroscience for interpretable data results and testable hypothesis generation.
If time allows, I will present proposed PCS inference that includes perturbation intervals and PCS hypothesis testing. The PCS inference uses prediction screening and takes into account both data and model perturbations.
Finally, a PCS documentation is proposed based on Rmarkdown, iPython, or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

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