将朴素贝叶斯训练分类器保存在 NLTK 中

我对如何保存经过训练的分类器有点困惑。例如，每次我想使用分类器时重新训练它显然非常糟糕且缓慢，我如何保存它并在需要时再次加载它？代码如下，提前感谢您的帮助。我正在使用 Python 和 NLTK 朴素贝叶斯分类器。

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)

To save:

import pickle
f = open('my_classifier.pickle', 'wb')
pickle.dump(classifier, f)
f.close()

稍后加载：

import pickle
f = open('my_classifier.pickle', 'rb')
classifier = pickle.load(f)
f.close()