1 answer

3. Suppose for a binary classification problem that you have a data set of 50 examples,...

Question:

3. Suppose for a binary classification problem that you have a data set of 50 examples, evenly split between positive and negative examples. If leave-one-out cross-validation is used, how will a simple majority classifier perform? Such a classifier outputs the class that is in the majority in the training set, regardless of the input. Please explain.

3. Suppose for a binary classification problem that you have a data set of 50 examples, evenly split between positive and negative examples. If leave-one-out cross-validation is used, how will a simple majority classifier perform? Such a classifier outputs the class that is in the majority in the training set, regardless of the input. Please explain.

Answers

Here in this case, simple majority classifier perform in in following way.

we will pickup only one example from dataset as test set. After then model is build on all the remaining examples of data set and then evaluation of error is being done on single example test set. We can reach to the generalized error estimation by repeating the same procedure with each of the example of dataset. and then averaging the results

.

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