Hi, not sure how this could generally work. However, you could at least dump the model parameters for e.g., linear models and compute the prediction via
w_1 * x1 + w_2 * x_2 + … + w_n * x_n + bias over the n features. To write various model attributes to text files, you could use json, e.g., see https://cmry.github.io/notes/serialize However, I don’t think that this approach will solve the problem of loading the model into C++. Best, Sebastian > On Apr 13, 2017, at 4:58 PM, 老陈 <26743...@qq.com> wrote: > > Hi, > > I am working on GradientBoostingRegressor these days and I am wondering if > there is a way to dump the model into txt file, or any other format that can > be processed by c++ > > My production system is in c++, so I want use the python-trained tree model > in c++ for production. > > Has anyone ever done this before? > > thanks > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn
