Classification of the reviews

At the beginning of this section, we will try to classify the corpus using algorithms we have already discussed (Naïve Bayes and k-NN). We will then briefly discuss two new algorithms: logistic regression and support vector machines.

Document classification with k-NN

We know k-Nearest Neighbors, so we'll just jump into the classification. We will try with three neighbors and five neighbors:

1  library(class) # knn() is in the class packages
2  library(caret) # confusionMatrix is in the caret package
3  set.seed(975)
4  Class3n = knn(TrainDF[,-1], TrainDF[,-1], TrainDF[,1], k = 3)
5  Class5n = knn(TrainDF[,-1], TrainDF[,-1], TrainDF[,1], k = 5)
6  confusionMatrix(Class3n,as.factor(TrainDF$quality))

The confusion matrix and the following statistics (the output has been partially reproduced) show that classification with three neighbors doesn't seem too bad: the accuracy is 0.74; yet, the kappa value is not good (it should be at least 0.60):

Confusion Matrix and Statistics...