I have a dataset with 4712 records.
Label Yes - 1558 records and
Label No - 3554 records.
I read online that
1:10 rule is based on the frequency of lower occurring class.
In my case, frequency of lower occurring class is 1558
According to 1:10 rule, am I right to understand that it is calculated like 1558/10 = 155.8 further equals 150 predictors
So In my logistic regression, I can use 150 variables/input features to the model without the risk of overfitting. Am I right?
By any chance do we also have to look at the
frequency of the other (high occuring) class to determine the no of predictors that I can use? If yes, can you share me as to what has to be done to determine the predictor count?
I am aware that we could also use
1:50 rule. But my question is mainly on
1) Whether is there any other consideration for determining the number of predictors in logistic regression model?
2) How do people calculate
min sample size required based on this?
Can someone help me with this?