New PDF release: Carrier System and Applications

February 14, 2018 | Data Mining | By admin | 0 Comments

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Of course, we want the model to do a good job of describing the data we have, but we are more interested in its performance with future data. 3 SCATTERPLOT SMOOTHED 57 In the example above, a simple straight line might do a better job than the complex function does of predicting future sales on the basis of advertising. Instead, we devised a complex function that fit the data perfectly, and in doing so, we overreached. We ended up “explaining” some variation in the data that was nothing more than chance variation.

2). We could connect up these points with a smooth but complicated function, one that explains all these data points perfectly and leaves no error (residuals). 3. , it is hard to believe that increasing expenditures from $400 to $500 will actually decrease revenue). 56 A basic purpose of building a model is to describe relationships among variables in such a way that this description will do a good job of predicting future outcome (dependent) values on the basis of future predictor (independent) values.

We can use classification techniques (logistic regression, classification trees, or other methods) to identify those individuals whose demographic and other data most closely matches that of our best existing customers. Similarly, we can use prediction techniques to forecast how much individual prospects will spend. 2. Which customers are most likely to commit, for example, fraud (or might already have committed it)? We can use classification methods to identify (say) medical reimbursement applications that have a higher probability of involving fraud and give them greater attention.

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