Machine Learning - (Baseline|Naive) classification (Zero R)

Thomas Bayes


A baseline classification uses a naive classification rule such as :

  • Base Rate (Accuracy of trivially predicting the most-frequent class). (The ZeroR Classifier in Weka) always classify to the largest class– in other words, classify according to the prior.
  • Random Rate (Accuracy of making a random class assignment, Might apply prior knowledge to assign random distribution)
  • Naïve Rate (Accuracy of some simple default or pre-existing model (Titanic example: “All females survived”)

It gives a baseline accuracy that must be always checked before choosing a sophisticated classifier. (Simplicity first).

The baseline accuracy is also known as the null rate.

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