Comparing multi-class vs. binary classifiers in predicting a single class
In general we can't compare the performance of a multiclass classifier with the performance of a binary classifier since the former expresses how good the classifier is at classifying any instance of any class. So if there are $n_A$ samples labelled A, there's only a proportion of $n_A/n$ of the global accuracy of the multiclass classifier which is about A. In particular a multiclass classifier usually tends to favor the largest classes, so if class A happens to be a small proportion of the data then the global performance will not reflect how good it is at classifying A: for example it might have 90% accuracy simply because class B is 90% of the data, this doesn't prove anything about class A. By contrast the performance of the binary classifier is by definition solely about class A.
However if one has access to the detailed evaluation of the multiclass classifier, typically the confusion matrix, then it becomes possible to calculate the performance of the classifier for a single class, say class A. Actually by merging all the B,C,D rows together and all the B,C,D columns together in the confusion matrix one obtains exactly a binary classification confusion matrix, and from that one can calculate a performance which can be compared against another binary classifier. But in this setting the multiclass classifier is at a disadvantage for the reason mentioned above: it also has to deal with the other classes and this can cause it to &quot;sacrifice&quot; a class, whereas the binary classifier doesn't have this issue.