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The decision tree in Figure 7.24 was built from a data set of 352 cars, using the continuous variable MPG to split the observations. The average value shown in the diagram is the MPG value for the set of observations. The nodes were not split further if there were less than 30 observations in the terminal node. In Table 7.17, a set of 36 observations not used in building the tree are shown with both an actual and a predicted value. The nal column indicates the node in the tree that was used to calculate the prediction. For example, the AMC Gremlin with a Horsepower of 90 and Weight of 2648 will t into a region de ned by node D in the tree. Node D has an average MPG value of 23.96 and hence this is the predicted MPG value. The table also indicates the actual MPG values for the cars tested. The examples used in this section were simple in order to describe how predictions can be made using decision trees. It is usual to use larger numbers of descriptor variables. Also, building a series of models based on changing the terminating criteria can also be useful in optimizing the decision tree models. The further reading section of chapter 6 provides references to additional methods for optimizing decision trees. The terminal nodes in the decision trees can be described as rules, (as shown in Section 6.3) which can be useful in explaining how a prediction was obtained. In addition, looking at the data that each rule is based on allows us to understand the degree of con dence with which each prediction was made. For example, the

Table 6.12 INPUT Title The Controls of the Registration Form TYPE OF CONTROL Radio button TAG TO BE USED <input type = radio name= value= >Display name <input type= text name= > <input type= text name= >

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Weighting Many longitudinal surveys provide data users with two types of weights cross-sectional weights and longitudinal weights The distinction reflects a difference in the population to be represented, which in turn is related to different estimation objectives, and in the sample cases that can contribute to estimates Given the focus of longitudinal surveys on longitudinal estimation, the longitudinal weights are perhaps the more important of the two However, it is not always obvious for which sets of respondents these should be produced There are 2t 1 possible populations that can be represented by a t-wave longitudinal survey, of which t are cross-sectional populations and 2t (t + 1) are longitudinal populations Potentially, a set of weights could be created for each population However, for surveys with more than two or three waves it would not be feasible to create all these sets of weights.

Unsupported processing. The lack of support in the servlet specification for the pre- and postprocessing of requests and responses prior to the 2.3 release is probably the most typical cause. Because so many Web applications had requirements to support functionality of this nature, Patterns and idioms developed to shoehorn pre- and postprocessing into existing implementations of the specification. The result was code that follows this AntiPattern. Habits. Another typical cause is developers not staying current and continuing to build applications with old approaches.

Similarly to the Bloated Session AntiPattern in 7, the God Object Web Service should be partitioned on the basis of cohesive abstraction. The Interface Partitioning refactoring in 3 can be applied to this situation in a similar way to subdivide the God Object Web Service into multiple Web Services that are more self-contained, standalone, and specific to a purpose or abstraction. In the case of Web Services, partitioning should be based on the specific document that the operations deal with or operate on for example, all the operations that deal with a Purchase Order entity, or a document, should be cohesively aggregated into one Web Service. Thus, CRUD-type operations, and possibly other related operations, such as status changes, should all be placed together. This process of partitioning should be followed for any other entities, documents, or target data items that the operations deal with.

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