sklearn-onnx converts many scikit-learn models into ONNX. Every of them is tested against a couple of runtimes. The following pages shows which models are correctly converted and compares the predictions obtained by every runtime. It also displays some benchmark. The benchmark evaluates every model on a dataset inspired from the Iris dataset, so with four features, and different number of observations N= 1, 10, 100, 1000, 100.00, 100.000. The measures for high values of N may be missing because the first one took too long.
skl_converters/bench_python skl_converters/bench_onnxrt skl_converters/bench_onnxrt_whole
All results were obtained using out the following versions of modules below:
from mlprodict.onnxrt.validate_helper import modules_list from pyquickhelper.pandashelper import df2rst from pandas import DataFrame print(df2rst(DataFrame(modules_list())))