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Chapter 13. The Experimenter > 13.3. Advanced setup

13.3 Advanced setup

The Experimenter has an advanced mode. Click near the top of the panel shown in Figure 13.1(a) to obtain the more formidable version of the panel shown in Figure 13.3. This enlarges the options available for controlling the experiment, including, for example, the ability to generate learning curves. However, the advanced mode is hard to use, and the simple version suffices for most purposes. For example, in the advanced mode you can set up an iteration to test an algorithm with a succession of different parameter values, but the same effect can be achieved in simple mode by putting the algorithm into the list several times with different parameter values.


FIGURE 13.3 Setting up an experiment in advanced mode.

One thing you can do in advanced mode but not in simple mode is run experiments using clustering algorithms. Here, experiments are limited to those clusterers that can compute probability or density estimates, and the main evaluation measure for comparison purposes is the log-likelihood. To set this up quickly, first click the Result generator to bring up an object editor for the CrossValidationResultProducer. Then click the Choose button for the split evaluator and select DensityBasedClustererSplitEvaluator from the list. At this point the panel on the lower right that contained the list of classifiers goes blank and the Generator properties dropdown box displays Disabled. Reenable this, and a new window appears with a list of properties (Figure 13.4(a)). Expand the splitEvaluator entry, select clusterer (as shown in the figure), and click the Select button. Now the active list will reappear in the bottom right panel, along with the ability to add clustering schemes, just as we did with classifiers.


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