"It's not denial. I'm just selective about the reality I accept."
- Bill Watterson
If the machine learning models were humans, they would have believed that the end justifies the means. When 99% of their training data belongs to one class, and their aim is to optimize their objective function, we cannot blame them if they focus on getting that single class right since it contributes to 99% of the solution. In the previous section, we tried to change this behavior by giving more weights to the minority class, or classes. Another strategy might entail removing some samples from the majority class or adding new samples to the minority class until the two classes are balanced.
Undersampling the majority class
"Truth, like gold, is to be obtained not by its growth, but by washing...
