Save MinMaxScaler model in sklearn

Question

I\'m using the MinMaxScaler model in sklearn to normalize the features of a model.

training_set = np.random.rand(4,4)*10
training_set

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Answer 1

The best way to do this is to create an ML pipeline like the following:

from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import MinMaxScaler
from sklearn.externals import joblib


pipeline = make_pipeline(MinMaxScaler(),YOUR_ML_MODEL() )

model = pipeline.fit(X_train, y_train)

Now you can save it to a file:

joblib.dump(model, 'filename.mod') 

Later you can load it like this:

model = joblib.load('filename.mod')