python - Preserving naive Bayesian training classifier in nltk

I'm slightly confused in regard to how I save a trained classifier. As in, re-training a classifier each time I want to use it is obviously really bad and slow, how do I save it and the load it again when I need it? Code is below, thanks in advance for your help. I'm using Python with NLTK Naive Bayes Classifier.

classifier = nltk.NaiveBayesClassifier.train(training_set)
# look inside the classifier train method in the source code of the NLTK library

def train(labeled_featuresets, estimator=nltk.probability.ELEProbDist):
    # Create the P(label) distribution
    label_probdist = estimator(label_freqdist)
    # Create the P(fval|label, fname) distribution
    feature_probdist = {}
    return NaiveBayesClassifier(label_probdist, feature_probdist)
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