Let's assume we want to integrate this classifier into our site. In all of the preceding examples, we always trained on only 90% of the available data, because we used the other 10% for testing. Let's assume that the data was all we had. In that case, we should retrain the classifier on all data:
>>> C_best = 0.01 # determined above
>>> clf = LogisticRegression(C=C_best)
>>> clf.fit(X, Y) # now trainining an all data without cross-validation
>>> print(clf.coef_)
[[ 0.24937413 0.00777857 0.0097297 0.00061647 0.02354386 -0.03715787 -0.03406846]]
Finally, we should store the trained classifier, because we definitely do not want to retrain it each time we start the classification service. Instead, we can simply serialize the classifier after training and then deserialize on that site:
>>> import pickle
>>> pickle...