410 CHAPTER11 The Explorer (a) (b) FIGURE11.3 The Weka Explorer: (a) choosing the Explorer interface and (b) reading in the weather data. this case the histogram will show the distribution of the class as a function of this attribute (an example appears later in Figure 11.10). You can delete an attribute by clicking its checkbox and using the Remove button. All selects all the attributes, None selects none, Invert inverts the current selection, and Pattern selects those attributes of which the names match a user-supplied regular expression. You can undo a change by clicking the Undo button. The Edit button brings up an editor that allows you to inspect the data, search for particular values and edit them, and delete instances and attributes. Right-clicking on values and column headers brings up corresponding context menus. BuildingaDecisionTree To see what the C4.5 decision tree learner described in Section 6.1 (page 201) does with this dataset, use the J4.8 algorithm, which is Weka's implementation of this decision tree learner. (J4.8 actually implements a later and slightly improved version called C4.5 revision 8, which was the last public version of this family of algorithms before the commercial implementation C5.0 was released.) Click the Classify tab to get a screen that looks like Figure 11.4(b). Actually, the figure shows what it will look like after you have analyzed the weather data. First select the classifier by clicking the Choose button at the top left, opening up the trees section of the hierarchical menu in Figure 11.4(a), and finding J48. The menu structure represents the organization of the Weka code into modules, which is described in Chapter 14 (page 519). For now, just open up the hierarchy as