Importing PMML models into Python (Scikit-learn)
<p>There seem to be a few options for exporting PMML models out of scikit-learn, such as sklearn2pmml, but a lot less information going in the other direction.  My case is an XGboost model previously built in R, and saved to PMML using r2pmml, that I would like to use in Python.  Scikit normally uses pickle to save/load models, but is it also possible to import models into scikit-learn using PMML?</p>

<p>You can't connect different specialized representations (such as R and Scikit-Learn native data structures) over a generalized representation (such as PMML). You may have better luck trying to translate R data structures to Scikit-Learn data structures directly.</p>

<p>XGBoost is really an exception to the above rule, because its R and Scikit-Learn implementations are just thin wrappers around the native XGBoost library. Inside a trained R XGBoost object there's a blob <code>raw</code>, which is the model in its native XGBoost representation. Save it to a file, and load in Python using the <code>xgb.Booster.load_model(fname)</code> method. </p>

<p>If you know that you need to the deploy XGBoost model in Scikit-Learn, then why do you train it in R?</p>

python scikit-learn pmml