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You will find PackageMaker in /Developer/Applications/Utilities. Open up the application. The first things you are asked for are your organization and the minimum OS version you are targeting. The organization value is used to set the names of preferences and similar files in Library/Preferences and other folders. See Figure 15 7.

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Once past that initial window, you need to start setting up the installer project. There are essentially two files that you need to think about here: the PackageMaker document, which is your collection of configuration options that are used to create the installation package, and the installation package itself. The various options that you ll be setting here will be stored in the PackageMaker document file (stored with a .pmdoc extension) and that document is used to provide the data to build the installation package file (stored with a .pkg extension). You can save the PackageMaker document file any time you want, and like any file it s worth periodically saving your work in case things go wrong. Start now and save the document file in a convenient location with a sensible name. I chose DailyJournal installer as the name.

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On training data On test data ---On test data (during pruning) - - - - 100

The first thing to do is to add the payload the application itself. Find the DailyJournal.app that you recently built (it will be in the project folder, in build/Release) and drag the application file into the left pane of the PackageMaker window. The icon and title appear in that pane (Figure 15 8).

Size of tree (number of nodes)

Effect of reduced-errorpruning in decision tree learning This plot shows the same curves of training and test set accuracy as in Figure 36 In addition, it shows the impact of reduced error pruning of the tree produced by ID3 Notice the increase in accuracy over the test set as nodes are pruned from the tree Here, the validation set used for pruning is distinct from both the training and test sets

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CAUTION: Notice the Allow Relocation option circled in Figure 15 8. This should not be checked, though it is by default. Leaving it checked will result in your installation failing and failing silently, which is even worse. So unchecking this option is a high priority!

multiple ways, then averaging the results Empirical evaluations of alternative tree pruning methods are reported by Mingers (1989b) and by Malerba et al (1995)

In practice, one quite successful method for finding high accuracy hypotheses is a technique we shall call rule post-pruning A variant of this pruning method is used by C45 (Quinlan 1993), which is an outgrowth of the original ID3 algorithm Rule post-pruning involves the following steps:

Turn to the Configuration tab. In most cases you won t need to change these settings, but understanding them is crucial. The Install: setting specifies the location of the payload the app file that you just added to the package. The destination is /Applications by default. If you keep this option you should also make sure that Require admin authentication is also checked (because most Macs require admin privileges to install into /Applications). It s also a good idea to leave the Allow custom location option checked, since this gives the user some flexibility if she wants to install the application elsewhere. For most applications, you should not require the user to restart her Mac. See Figure 15 9.

1 Infer the decision tree from the training set, growing the tree until the training data is fit as well as possible and allowing overfitting to occur 2 Convert the learned tree into an equivalent set of rules by creating one rule for each path from the root node to a leaf node 3 Prune (generalize) each rule by removing any preconditions that result in improving its estimated accuracy 4 Sort the pruned rules by their estimated accuracy, and consider them in this sequence when classifying subsequent instances

To illustrate, consider again the decision tree in Figure 31 In rule postpruning, one rule is generated for each leaf node in the tree Each attribute test along the path from the root to the leaf becomes a rule antecedent (precondition) and the classification at the leaf node becomes the rule consequent (postcondition) For example, the leftmost path of the tree in Figure 31 is translated into the rule IF THEN (Outlook = Sunny) A (Humidity = High) PlayTennis = No