A supervised learning model has been built to predict whether someone is infected with a new strain of a virus. The probability
of any one person having the virus is 1%. Using accuracy as a metric, what would be a good choice for a baseline accuracy score that the new model would want to outperform
Since this is an example of a classification problem (the classes being whether somebody has been infected with a new virus or not), the ideal score to achieve in such a case is 100%. Hence, a baseline score of 99% should be set in order to get to 100% by outperforming it.
