COMP809 – Data Mining and Machine Learning Week 8– Naive Bayes Classifier
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COMP809 – Data Mining andMachine Learning
Week 8– Naive Bayes Classifier
Ø Two major objectives of this lab are to
o configure Python’s implementation of Naive Bayes classifier
o to evaluate these classifiers using a variety of different metrics.
Ø Configuring classifiers will be achieved using Python’s sklearn library. Evaluation will also be done via sklearn but use a dedicated set of methods designed specifically for computing metrics.
Same Dataset with Lab 7.
We had made some experiments about Decision Tree classifier in Lab 7.
• Configure the Naive Bayes classifier with the Gaussian option for numeric data
gnb = GaussianNB() #suitable for numeric features
gnb.fit(pred_train, np.ravel(tar_train,order='C')) predictions
= gnb.predict(pred_test)
print("Accuracy score of our model with Gaussian Naive
Bayes:", accuracy_score(tar_test, predictions))
• Configuring the Multinomial Naïve Bayes Classifier
By referring suitable information online identify the version of Naive Bayes suitable for classifying discrete data and fill in the line below.
mnb = MultinomialNB() #optimized for nominal features but can work for numeric ones as well
mnb.fit(pred_train, np.ravel(tar_train,order='C'))
predictions = mnb.predict(pred_test)
print("Accuracy score of our model with Multinomial Naive Bayes:", accuracy_score(tar_test, predictions))
• Present the model results to a table in a word document, like the given examples.
The result table with the accuracy is usually not enough. It is recommended to calculate and provide metrics such as precision, recall, F Measure and confusion matrix in a report.
2023-06-29