High performance, easy-to-use, and scalable machine learning (ML) package, including linear model (LR), factorization machines (FM), and field-aware factorization machines (FFM) for Python and CLI interface.
In this PR, we add a Popper workflow for automatically downloading and verifying the complete HIGGS data set from UCI (which has 11 million entries), running the benchmark to compare liblinear vs xlearn, and finally generating a report with a chart that shows the results including error bars.
Through this PR,
We add a popper folder that includes all the scripts, the workflow file wf.yml and a README.md that explains how to run it.
We also modified the README.rst from the Higgs demo to include information about the workflow using Popper.
In this PR, we add a Popper workflow for automatically downloading and verifying the complete HIGGS data set from UCI (which has 11 million entries), running the benchmark to compare
liblinearvsxlearn, and finally generating a report with a chart that shows the results including error bars.Through this PR,
popperfolder that includes all the scripts, the workflow filewf.ymland aREADME.mdthat explains how to run it.README.rstfrom the Higgs demo to include information about the workflow using Popper.