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I am taking classes on intro to AI,and the teacher mentioned some point that for the classifier ZeroR,the accuracy under ZeroR is a helpful baseline for interpreting other classifiers.
I searched online about this but still couldn’t get my head around it,could anyone give some idea on what that means please,thanks in advance.
I think the line of reasoning runs as follows: A ZeroR classifier simply assigns every value to the most common class (as found by examining the training data). This means that if your data is 55% class A, 10% class B, 5% class C etc then ZeroR will get 55% right. If your data is 33% class A, 31% class B, 28% class C etc then ZeroR will get 33% right.
Save from randomly selecting classes, this is pretty much the dumbest classifier you can get and so you can measure other classifiers by how well they do compared to this minimal level of performance. Given a certain data set, you can use ZeroR to find out what the minimum performance is you may expect.