Update page 'Questions concerning R language'
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*###Applied*
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***Applied***
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This problem involves the `OJ` data set which is part of the `ISLR` package.
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* Create a training set containing a random sample of 800 observations, and a test set containing the remaining observations.
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* Fit a support vector classifier to the training data using `cost=0.01`, with `Purchase` as the response and the other variables as predictors. Use the `summary()` function to produce summary statistics, and describe the results obtained.
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* Repeat parts (ii) through (v) using a support vector machine with a polynomial kernel. Set `degree=2`.
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* Overall, which approach seems to give the best results on this data?
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*###Conceptual*
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***Conceptual***
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Here we explore the maximal margin classifier on a toy data set.
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* We are given n = 7 observations in p = 2 dimensions. For each observation, there is an associated class label.
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| Obs. | X1 | X2 | Y |
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