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?
1000 Class = 1
vs. 1000 Class = 0
or
1000 Class = 1
vs. the whole Class = 0
as 299.000?