Saving a cross-validation trained model in Scikit

Question

I have trained a model in scikit-learn using Cross-Validation and Naive Bayes classifier. How can I persist this model to later run against new instances?

Here is simply what I have, I can get the CV scores but I don’t know how to have access to the trained model

gnb = GaussianNB() 
scores = cross_validation.cross_val_score(gnb, data_numpy[0],data_numpy[1], cv=10)

Asked By: Ali

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Source

Answer 1

cross_val_score doesn’t changes your estimator, and it will not return fitted estimator. It just returns score of estimator of cross validation.

To fit your estimator – you should call fit on it explicitly with provided dataset.
To save (serialize) it – you can use pickle:

# To fit your estimator
gnb.fit(data_numpy[0], data_numpy[1])
# To serialize
import pickle
with open('our_estimator.pkl', 'wb') as fid:
    pickle.dump(gnb, fid)
# To deserialize estimator later
with open('our_estimator.pkl', 'rb') as fid:
    gnb = pickle.load(fid)

Answered By: Ibraim Ganiev

Answer 2

I could be mistaken about multioutput.RegressorChain()’s internals, but I believe you could supply RegressorChain w/ the same cv and run RegressorChain w/ just one dv.

That would allow you to use .predict() as you’d like.

Answered By: Nicholas Judge

Saving a cross-validation trained model in Scikit

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