How do I save a trained Naive Bayes classifier to disk and use it to predict data?
I have the following sample program from the scikit-learn website:
from sklearn import datasets
iris = datasets.load_iris()
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
y_pred = gnb.fit(iris.data, iris.target).predict(iris.data)
print "Number of mislabeled points : %d" % (iris.target != y_pred).sum()
Best Answer
Classifiers are just objects that can be pickled and dumped like any other. To continue your example:
Edit: if you are using a sklearn Pipeline in which you have custom transformers that cannot be serialized by pickle (nor by joblib), then using Neuraxle's custom ML Pipeline saving is a solution where you can define your own custom step savers on a per-step basis. The savers are called for each step if defined upon saving, and otherwise joblib is used as default for steps without a saver.