Naive Bayes classifier
Bright Wire includes two separate Naive Bayes classifiers that are designed expressly to classify text: Multinomial Naive Bayes and Bernoulli Naive Bayes. Once we have the training and test sets we can train and evaluate the two Naive Bayes classifiers. The Bernoulli and Multinomial Naive Bayes classifiers get a score of around 83% and 84% respectively on the test data set. It turns out that we can do a little better than Naive Bayes with a simple feed forward neural network. The Neural Network ends up with a slightly higher accuracy compared to the Naive Bayes classifiers. Next we add the Bernoulli and Multinomial Naive Bayes classifiers to the graph along two separate wires.
We'll start with a Naive Bayes classifier. It turns out that both the Naive Bayes classifier and the Decision Tree get exactly the same accuracy - around 97%. Naive Bayes and Decision Trees aren't linear algebra based machine learning algorithms (they don't use vectors or matrices).