Which of the following classifiers do not require model selection? Nearest neigh
ID: 3890581 • Letter: W
Question
Which of the following classifiers do not require model selection?
Nearest neighbor classifiers.
A linear maximum margin classifiers.
Decision tree classifiers.
None of the above require model selection.
All of the above require model selection.
Cross validation, applied to a given classifier, can be used for various purposes, but it always estimates a specific quantity. Which one?
The classifier parameters.
The optimal complexity of the model.
The prediction error of the classifier.
The model hyperparameters.
Recall that K-fold cross validation removes a validation set, then splits up the remaining data into K (roughly) equally sized blocks. How large should the blocks be chosen?
Each block should be as large as possible.
Each block should be as small as possible.
There is a trade-off, since both large and small blocks have advantages.
The block size does not matter, as long as the split is random.
Explanation / Answer
1.All of the above have model selection since we have to choose various different parameters for given algorithms
answer :All of above has model selection
2.cross validation is used to select models using prediction error of classifiers.
Hence answer :The prediction error of the classifier.
3.If the test block is large then there is loss data for train set.similarly for vice versa condition
Hence answer:There is a trade-off, since both large and small blocks have advantages.
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