machine-learning An introduction to Classificiation: Generating several models using Weka Train the first classifier: Setting a baseline with ZeroR
Example
ZeroR is a simple classifier. It doesn't operate per instance instead it operates on general distribution of the classes. It selects the class with the largest a priori probability. It is not a good classifier in the sense that it doesn't use any information in the candidate, but it is often used as a baseline. Note: Other baselines can be used aswel, such as: Industry standard classifiers or handcrafted rules
// First we tell our data that it's class is hidden in the last attribute
data.setClassIndex(data.numAttributes() -1);
// Then we split the data in to two sets
// randomize first because we don't want unequal distributions
data.randomize(new java.util.Random(0));
Instances testset = new Instances(data, 0, 50);
Instances trainset = new Instances(data, 50, 99);
// Now we build a classifier
// Train it with the trainset
ZeroR classifier1 = new ZeroR();
classifier1.buildClassifier(trainset);
// Next we test it against the testset
Evaluation Test = new Evaluation(trainset);
Test.evaluateModel(classifier1, testset);
System.out.println(Test.toSummaryString());
The largest class in the set gives you a 34% correct rate. (50 out of 149)
Note: The ZeroR performs around 30%. This is because we splitted randomly into a train and test set. The largest set in the train set, will thusly be the smallest in the test set. Crafting a good test/train set can be worth your while
