Save MinMaxScaler model in sklearn

Solution 1:

Even better than pickle (which creates much larger files than this method), you can use sklearn's built-in tool:

from sklearn.externals import joblib
scaler_filename = "scaler.save"
joblib.dump(scaler, scaler_filename) 

# And now to load...

scaler = joblib.load(scaler_filename) 

Note: sklearn.externals.joblib is deprecated. Install and use the pure joblib instead

Solution 2:

So I'm actually not an expert with this but from a bit of research and a few helpful links, I think pickle and sklearn.externals.joblib are going to be your friends here.

The package pickle lets you save models or "dump" models to a file.

I think this link is also helpful. It talks about creating a persistence model. Something that you're going to want to try is:

# could use: import pickle... however let's do something else
from sklearn.externals import joblib 

# this is more efficient than pickle for things like large numpy arrays
# ... which sklearn models often have.   

# then just 'dump' your file
joblib.dump(clf, 'my_dope_model.pkl') 

Here is where you can learn more about the sklearn externals.

Let me know if that doesn't help or I'm not understanding something about your model.

Note: sklearn.externals.joblib is deprecated. Install and use the pure joblib instead