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Consider the data with categorical predictor x1 = {green or red} and numerical predictor x2 and the class variable y shown in the following table. The weights for a round of boosting are also shown in the table. Suppose the classifier built from this round of boosting assigns y = 1 for x2 < 0.45 and y = -1 for x2 > 0.45. Compute the coefficient α for this classifier in the adaboost algorithm.
The next type of input is a Pull Down List. With this type you have to employ in place of and it also has a closing tag. This control is used when we have a
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Automatic correlation from web point of sight can be set in recording options and correlation tab. Here we can enable correlation for the whole script and choose either issue onlin
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This is a huge collection of computational algorithms ranging from elementary functions like sum, sine, cosine, and difficult arithmetic, to more sophisticated functions like matri
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