question 17 why are practitioners rarely concerned with the issues of representing and visualizing the decision boundaries for classification problems
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Decision boundaries are the regions in the feature space that separate different classes. They are crucial for understanding how a classifier makes decisions. Show more…
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The scatterplot below shows the values of two variables for 300 observations from each of two classes (600 total). The observations are marked by class — either, O or X: a) Five points are marked with the labels A, B, C, D, and E. If the two classes occur equally often, and the cost of misclassification is the same for both classes, how would you classify each of these five points? Write O or X for each point, or write U if you can’t tell clearly from the information shown. No explanation is required. A: B: C: D: E: b) Suppose that we reduced these observations from two variables to a single variable by projecting them on the first principal component direction, as found using all 600 observations, from both classes. How well would you expect to be able to classify a new observation on the basis of its projection on this first principal component? Would it work just as well as with the original variables? Or almost as well? Or much worse? Or perhaps classification would not be possible at all? Explain.
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