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We begin by applying the decision trees algorithms, CART and C5.0, using 14.2 1 misclassi cation costs, and 50% 50% balancing. The results are provided in Table 7.18. Note that both models have already outperformed the best of the PCA models, with an estimated pro t per customer of $3.04 and $3.01, compared to $2.96 for the logistic regression PCA model. Suppose, however, that we wished to enrich our pool of algorithms to include those without built-in misclassi cation costs. Then we can apply overbalancing as a surrogate for misclassi cation costs, just as we did for the PCA models. Table 7.19 contains the performance results from all four algorithms, using 80% overbalancing. Note the wide disparity in model performance. Here, C5.0 is the winner, with a solid estimated pro t of $3.15, representing the best overall prediction performance by a single model in this case study. The logistic regression model is not far behind, at $3.12. The neural network model, however, performs relatively poorly, at only $2.78. (It should be noted here that all neural network models run in this case study used Clementine s default settings and the quick option. Perhaps the neural network performance could be enhanced by tweaking the many settings and options available.) Next, we combine the four models, rst through the use of voting. Table 7.20 provides the performance metrics from the four methods of counting the votes,

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these problems were addressed in later revisions (Greer & Lacy, 1989; Knoppers, Zuidema, & Meyer, 1989; Lacy & Greer, 1992), researchers have moved on to other approaches More recently, Lee, Whitehead, and Balchin (2000; Lee, Whitehead, Hatzigeorgiadis, & Ntoumanis, 2001) have sought to renew interest in values among sport psychologists and have developed the Youth Sport Values Questionnaire Value items were derived from analysis of moral dilemma interview data Pilot tests have demonstrated that respondents generally valued enjoyment and personal achievement over winning, and that value rankings were consistent across subgroups based on gender, age, sport type, and level of performance Sportspersonship When people think of morality and sport, often what first comes to mind is sportspersonship Sportspersonship has been a topic of considerable, but intermittent, interest to sport psychologists who have developed a large number of inventories assessing it over the years (eg.

, Haskins, 1960; Johnson, 1969; Lakie, 1964; Vallerand, Briere, Blanchard, & Provencher, 1997) Sportspersonship can be thought of both as a set of attitudes and as behavior that gives expression to those attitudes Early assessments of sportspersonship often targeted attitudes (eg, Kistler, 1957; Lakie, 1964) It is beyond our scope to review each of these scales separately, nor is there need to do so Very few have been used beyond pilot investigations, and most did not meet minimal criteria for psychometric adequacy (Bredemeier & Shields, 1998) Though researchers have had difficulty operationalizing the term, most people would say about sportspersonship what Supreme Court Judge Potter Stewart said about pornography: I know it when I see it In fact, there appears to be a fair bit of commonsense agreement about the behavioral meaning of the term.

Comparing a list of conceptions offered by children in 1963 (see Bovyer, 1963) with a list compiled nearly 3 decades later (see Entzion, 1991) reveals remarkable consistency in the basic contents of sportspersonship Children s conceptions of the term include adherence to norms and conventions that serve to maintain the social order (eg, take turns, follow the rules), as well as more explicitly moral concerns (eg, don t hurt anybody, don t cheat, don t brag) To remedy some of the difficulties that have plagued sportspersonship research, Vallerand and his colleagues sought to behaviorally define and measure sportspersonship In the first phase of their investigation, they asked.

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// Read a covert char and put in overt2 file, // until covert file exhausted (normal ending) // or overt file exhausted (error) done = FALSE; while(done == FALSE) { read_cnt = fread( &ch, sizeof(char), 1, covertStream ); if(read_cnt > 0) { write_cnt = fwrite( &ch, sizeof(char), 1, overt2Stream ); if(write_cnt < 1) { printf( Error, overt2 file too small\n ); exit(0); } // Move the pointer into the overt file // forward one character, so that docs // have the same size. read_cnt = fread( &ch, sizeof(char), 1, overtStream ); } else { done = TRUE; } } // Write remaining overt file to overt2... copyRemainingOvert(); return SUCCESS; } //doc_stuffer_encode();

Figure 1-3

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