Machine Learning in Java - Second Edition

Implementing the Naive Bayes baseline

Now, when we have all of the ingredients, we can replicate the Naive Bayes approach that we are expected to outperform. This approach will not include any additional data preprocessing, attribute selection, or model selection. As we do not have true labels for the test data, we will apply five-fold cross-validation to evaluate the model on a small dataset.

First, we initialize a Naive Bayes classifier, as follows:

Classifier baselineNB = new NaiveBayes(); 

Next, we pass the classifier to our evaluation function, which loads the data and applies cross-validation. The function returns an area under the ROC curve score for all three problems, and the overall results:

double resNB[] = evaluate(baselineNB); ...