I wanted to convert a trained Scikit-learn logistic regression model into a Core ML format model which could be use in iOS.

Apple provide a Python package coremltools which can be used to make this conversion.

I tried installing it using the instructions on the Apple documentation, and ran into an error message - it appears that Python version 2.7 is needed.

The following steps (this post was helpful!) worked OK on macOS Sierra:

First, install a virtualenv with Python 2.7:

# If virtualenv not already installed globally:

pip install virtualenv

# Then create the virtual environment and activate it:

virtualenv --python=/usr/bin/python2.7 pyv27
source pyv27/bin/activate

Next install the required packages into this virtual environment:

pip install -U coremltools

pip install scikit-learn

Then run the conversion from a Python script (clf is the name of this scikit-learn model):

import coremltools

coreml_model = coremltools.converters.sklearn.convert(clf, ["Age"], "Breakdown")

coreml_model.save("LogRegModel.mlmodel")

The documentation suggests evaluating the model with some predictions (model.predict)… but it appears this is not supported in my version of MacOS…

Next step is to try out this model in CoreML and Swift.