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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? The 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)

1 Answer

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by (33.2k points)

You can use Python’s Pickle library to save most of the machine learning model and you can also restore the saved models later using same library.

To save the model:

import pickle

f = open('my_classifier.pickle', 'wb')

pickle.dump(classifier, f)


To load/restore the saved model:

import pickle

f = open('my_classifier.pickle', 'rb')

classifier = pickle.load(f)


I hope this answer would help

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