Leave-one-out-cross-validation query

Question

I am currently trying to understand a topic in Artificial Intelligence (Learning) and need assistance in understanding the following:

Why would a Leave-one-out-cross-validation algorithm, when used in conjunction with a majority classifier, score zero instead of 50% on a data set of equal number of positive and negative examples?

Thank you for your guidance on this.

Answer 1

If I understand the question correctly, when you leave out the positive sample, the training set has more negative samples; therefore the left out sample is classified as negative. And vice versa.