"MultinomialNBclassifier", "BernoulliNB", "LogisticRegression_classifier", "SGDClassifier_classifier ", "SVC_classifier", "LinearSVC_classifier", "NaiveBayesClassifier" ] MultinomialNBclassifier = SklearnClassifier(MultinomialNB()) MultinomialNBclassifier.train(train_data) print("\nMultinomialNB Accuracy is:", (classify.accuracy(MultinomialNBclassifier, test_data)) * 100) # GaussianNBclassifier = SklearnClassifier(GaussianNB()) # GaussianNBclassifier.train(train_data) # print("\nGaussianNB Accuracy is:", classify.accuracy(GaussianNBclassifier, test_data)) BernoulliNB = SklearnClassifier(BernoulliNB()) BernoulliNB.train(train_data) print("BernoulliNB Algo Accuracy: ", (nltk.classify.accuracy(BernoulliNB, test_data)) * 100) LogisticRegression_classifier = SklearnClassifier(LogisticRegression()) LogisticRegression_classifier.train(train_data) print("LogisticRegression Algo Accuracy: ", (nltk.classify.accuracy(LogisticRegression_classifier, test_data)) * 100) SGDClassifier_classifier = SklearnClassifier(SGDClassifier()) SGDClassifier_classifier.train(train_data) print("SGDClassifier Algo Accuracy: ", (nltk.classify.accuracy(SGDClassifier_classifier, test_data)) * 100) SVC_classifier = SklearnClassifier(SVC()) SVC_classifier.train(train_data)
pickle.dump(LinearSVC, save_classifier) save_classifier.close() ### Multinomial Naive Bayes MNB = SklearnClassifier(MultinomialNB()) MNB.train(training_set) print("Multinomial naive bayes accuracy percentage:", (nltk.classify.accuracy(MNB, testing_set)) * 100) save_classifier = open("pickled_algorithms/MNB_classifier.pickle", "wb") pickle.dump(MNB, save_classifier) save_classifier.close() ### Bernoulli naive Bayes BernoulliNB = SklearnClassifier(BernoulliNB()) BernoulliNB.train(training_set) print("Bernoulli naive bayes accuracy percentage:", (nltk.classify.accuracy(BernoulliNB, testing_set)) * 100) save_classifier = open("pickled_algorithms/BernoulliNB_classifier.pickle", "wb") pickle.dump(BernoulliNB, save_classifier) save_classifier.close() ### Logistic Regression LogisticRegression = SklearnClassifier(LogisticRegression()) LogisticRegression.train(training_set) print("LogisticRegression accuracy percentage:", (nltk.classify.accuracy(LogisticRegression, testing_set)) * 100) save_classifier = open(
print("Original Naive Bayes Accuracy Percentage: ", (nltk.classify.accuracy(classifier, test_set)) * 100) classifier.show_most_informative_features(30) #save_classifier = open("naivebayes.pickle", "wb") #pickle.dump(classifier, save_classifier) #save_classifier.close() MNB_classifier = SklearnClassifier(MultinomialNB()) MNB_classifier.train(train_set) print("MNB_classifier Accuracy Percentage: ", (nltk.classify.accuracy(MNB_classifier, test_set)) * 100) BernoulliNB = SklearnClassifier(BernoulliNB()) BernoulliNB.train(train_set) print("BernoulliNB Accuracy Percentage: ", (nltk.classify.accuracy(BernoulliNB, test_set)) * 100) LogisticRegression = SklearnClassifier(LogisticRegression()) LogisticRegression.train(train_set) print("LogisticRegression Accuracy Percentage: ", (nltk.classify.accuracy(LogisticRegression, test_set)) * 100) SGDClassifier = SklearnClassifier(SGDClassifier()) SGDClassifier.train(train_set) print("SGDClassifier Accuracy Percentage: ", (nltk.classify.accuracy(SGDClassifier, test_set)) * 100) SVC = SklearnClassifier(SVC()) SVC.train(train_set)