. You just started a small business. You lend money. People come to you, you look at their applications and you either grant them money, or you reject them. You need a model that helps you predict if an applicant is a good one or not. You have three options:

option 1: a model with 95% accuracy

option 2: a model with 95% precision

option 3: a model with 95% recall

option 4: a model with 95% f1_score

You read about the models. You find out all models are trained by imbalanced datasets. It is the start of your business, so you prefer to attract as many customers as possible to become well-known among competitors. Probably if you loose some money due to some bad candidates you granted loan to, you won't mind that much.

Based on all the above, which model will you choose? Remember, good applicants are noted by (0, or negative, or NO) and bad applicants by (1, positive, or YES).

a. model 1

b. model 2

c. model 3

d. model 4