I am quite new machine learning methods, so I may not write proper technical formulas.
My question is about the optimal proportion between sample size in
Class = 1 and
Class = 0 in a binary classification problem. (I might use different models, but I will start with decision tree).
I am trying to develop a risk model for a population of 300.000 by using some risk factors, and each risk factor has a risk-weight. So basically it is a accumulative model
Risk_amount = Risk_factor_1 * weight_1 + Risk_factor_2 * weight_2 + Risk_factor_3 * weight_3 ...
My big question is if a
Risk_amount really belongs to
Class = 1 or not.
I want my ML-model to find the most optimal weights to those risk factors.
I already know that 1000 out of 300.000 are risky in real. So the amount of target data with
Class = 1 is 1000.
So the total amount of target data with non-risky population
Class = 0 is 299.000
The question is what is the optimal proportion between the amount of
Class = 1 and the amount of Class = 0?
Class = 1 vs. 1000
Class = 0
Class = 1 vs. the whole
Class = 0 as 299.000?