Suppose that we record attributes for an animal whose label we do not know; we can predict its label from the learned classification model. We will use the following animal for this process:

First, we construct a feature vector describing the new specimen, as follows:

double[] vals = new double[data.numAttributes()]; 
vals[0] = 1.0; //hair {false, true} 
vals[1] = 0.0;  //feathers {false, true} 
vals[2] = 0.0;  //eggs {false, true} 
vals[3] = 1.0;  //milk {false, true} 
vals[4] = 0.0;  //airborne {false, true} 
vals[5] = 0.0;  //aquatic {false, true} 
vals[6] = 0.0;  //predator {false, true} 
vals[7] = 1.0;  //toothed {false, true} 
vals[8] = 1.0;  //backbone {false, true} 
vals[9] = 1.0;  //breathes {false, true} 
vals[10] = 1.0;  //venomous {false, true} 
vals[11] = 0.0;  //fins {false, true} 
vals[12] = 4.0;  //legs INTEGER [0,9] 
vals[13] = 1.0;  //tail {false, true} 
vals[14] = 1.0;  //domestic {false, true} 
vals[15] = 0.0;  //catsize {false, true} 
DenseInstance myUnicorn = new DenseInstance(1.0, vals);
myUnicorn.setDataset(data); 

Then, we call the classify(Instance) method on the model, in order to obtain the class value. The method returns the label index, as follows:

double result = tree.classifyInstance(myUnicorn); 
System.out.println(data.classAttribute().value((int) result)); 

This will provide the mammal class label as output.