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I am taking classes on an 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.

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ZeroR is the simplest classification method that relies on the target and ignores all predictors. ZeroR classifier simply predicts the majority category (class). In spite of the fact that there is no predictability power in ZeroR, it is useful for determining a baseline performance as a benchmark for other classification methods.

The algorithm goes by constructing a frequency table for the target and select its most frequent value.


"Play Golf = Yes" is the name for the ZeroR model for the following dataset with an accuracy of 0.64.


Predictors Contribution

We cannot say about the predictor's contribution to the model because   ZeroR does not use any of them.

Model Evaluation

The following confusion matrix shows that ZeroR only predicts the majority class correctly. As mentioned before, ZeroR is only useful for determining a baseline performance for other classification methods.

If you want to know ZeroR Classifier in Python visit this Python Course.

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