## Why Classification

As Gendler states (2011), “classifying objects into groups allows us to proceed effectively in an environment teeming with overwhelming detail.”

## Random Baseline

In order to compute the accuracy of a random baseline for a multi-class classfication you can simply: \[acc = P(y=0) \times P(\hat{y}=0) + P(class is 1) \times P(you guess 1) \] where P(y=1) means the probability that the class is actually 1, and \(P(\hat{y}=1)\) means tne probability that you guess 1.

## Majority-Class Classifier

This a baseline which is equal or better than random baseline (False. It can be worst) in which we always predict the majority class. It’s also called zero rule (ZeroR or 0R).