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How will we know when our models are performing well Is 80% classi cation accuracy good enough 90% 95% To be able to calibrate the performance of our candidate models, we need to establish benchmarks against which these models can be compared These benchmarks often come in the form of baseline model performance for some simple models Two of these simple models are (1) the don t send a marketing promotion to anyone model, and (2) the send a marketing promotion to everyone model Clearly, the company does not need to employ data miners to use either of these two models Therefore, if after arduous analysis, the performance of the models reported by the data miner is lower than the performance of either of the baseline models above, the data miner better try again.

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A new column of registry keys appears. Double-click Software. A long column of registry keys will appear. Scroll down until you nd Microsoft, and then double-click it. Another long list of registry keys appears. Scroll down and doubleclick Windows NT. Click the CurrentVersion registry key. In the right window pane, double-click the RegisteredOwner registry value (see Figure 2-5).

3 Controls and Dialog Boxes In this chapter, we look at how to program some of the most fundamental ingredients of Windows CE

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In other words, the models reported by the data miner absolutely need to outperform these baseline models, hopefully by a margin large enough to justify the project Recall Table 72, the cost/bene t decision table for this case study Applying those costs and bene ts to these two baseline models, we obtain for the test data set (5908 negative responses and 1151 positive responses) the performance measures shown in Table 710 Which of these two baseline models performs better A comparison of the overall error rates would lead one to prefer the send to everyone model However, as we discussed earlier, data mining models need to take into account real-world considerations such as costs and bene ts, so that traditional model performance measures such as false positive rate, false negative rate, and overall error rate are deemphasized.

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29. Eizel Technologies, Inc., Ampli Enterprise Server, http://www.eizel.com. 30. W3C Recommendation: HTML 4.01 Speci cation, http://www.w3.org/TR/1999/REChtml401-19991224/, Dec. 1999. 31. W3C Working Draft, XHTML 2.0, http://www.w3.org/TR/2003/WD-xhtml220030506/, May 2003. 32. W3C Candidate Recommendation, CSS Media Queries, http://www.w3.org/TR/ css3-mediaqueries/, July 2002. 33. W3C Recommendation, Cascading Style Sheets level 2 CSS2 Speci cation, http:// www.w3.org/TR/1998/REC-CSS2-19980512/, May 1998. 34. OMA (formerly WAP Forum), Wireless Markup Language Speci cation 1.3, http:// www1.wapforum.org/tech/terms.asp doc WAP-191-WML-20000219-a.pdf, Feb. 2000. 35. M. Hudley, A Framework for Multilingual, Device-Independent Web Sites, Sun Developer Connection, April 2001, http://wwws.sun.com/software/xml/

Instead, we deal directly with the bottom line: What effect would the deployment of this model have on the pro tability of the company Here, the don t send to anyone model is costing the company an estimated $463 per customer in lost pro ts Of the customers in this data set, 163% would have responded positively to the direct mail marketing promotion had they only been given the chance Therefore, this model must be considered a complete failure and shall.

1.4. Key Technical Details Selection of the proper lot of fetal bovine serum (FBS) for use in culturing MSCs is perhaps the most important parameter in MSC technology [Lennon et al., 1996] and is described brie y in Section 9. It has been our experience that it is not good practice to purchase serum off the shelf (that is, without testing). We have also found that a batch of serum optimal for MSCs from one species will

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method uses fuzzy recognition rules for segmentation and recognition. The third method classi es a test grasp using HMMs, which are applied to the output cluster centers of the grasp models. It should be stressed that methods 1 and 2 are related; both of them use distances between fuzzy clusters for recognition. Method 3 is a completely different approach using a probability method for recognition and classi cation.

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