Data Mining: Practical Machine Learning Tools and Techniques, Second Edition
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- 15.2 Conventions for implementing classifiers
graphable. For more information on these and other interfaces, look at the Javadoc for the classes in weka.core. 15.2 Conventions for implementing classifiers There are some conventions that you must obey when implementing classifiers in Weka. If you do not, things will go awry. For example, Weka’s evaluation module might not compute the classifier’s statistics properly when evaluating it. The first convention has already been mentioned: each time a classifier’s buildClassifier() method is called, it must reset the model. The CheckClassifier class performs tests to ensure that this is the case. When buildClassifier() is called on a dataset, the same result must always be obtained, regardless of how often the classifier has previously been applied to the same or other datasets. However, buildClassifier() must not reset instance variables that correspond to scheme- specific options, because these settings must persist through multiple calls of buildClassifier(). Also, calling buildClassifier() must never change the input data. Two other conventions have also been mentioned. One is that when a classifier can’t make a prediction, its classifyInstance() method must return
probabilities of zero for all classes. The ID3 implementation in Figure 15.1 does this. Another convention is that with classifiers for numeric prediction, classi-
fiers, however, are able to predict nominal classes and their class probabilities, as well as numeric class values—weka.classifiers.lazy.IBk is an example. These implement the distributionForInstance() method, and if the class is numeric it returns an array of size 1 whose only element contains the predicted numeric value.
Another convention—not absolutely essential but useful nonetheless—is that every classifier implements a toString() method that outputs a textual descrip- tion of itself. 1 5 . 2
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