The vanilla approach is to directly apply the lesson, just like as it was demonstrated in Chapter 3, Basic Algorithms - Classification, Regression, Clustering, without any preprocessing, and not taking dataset specifics into account. To demonstrate the drawbacks of the vanilla approach, we will simply build a model with the default parameters and apply k-fold cross-validation.
First, let's define some classifiers that we want to test, as follows:
ArrayList<Classifier>models = new ArrayList<Classifier>(); models.add(new J48()); models.add(new RandomForest()); models.add(new NaiveBayes()); models.add(new AdaBoostM1()); models.add(new Logistic());
Next, we need to create an Evaluation object and perform k-fold cross-validation ...
