This question already has an answer here:
Lets say for fraud detection which has two labels for each transaction.
- Non fraud
In real world scenario we usually get more number of examples of Non fraud data points and very low number of fraud data points. Lets assume the ratio of Non fraud: fraud is 80:20. So my question is even if I build any classifier my model will predict the majority label but I know that data itself is not well distributed. So for such scenarios what should be the approach. Sorry for very trivial question I am completely naive in this field.