So I am not sure if this question makes sense at all, but I will try to explain.
I want to build a logistic regression scoring model that learns automatically (updates) when sample is updated with new data. I have done variable selection using correlations among variables, statistical tests and univariate analysis combined with apriori knowledge about variables and their potential influence on the response.
But I have one problematic variable, for which I know that it should be, say, strongly negatively correlated with response, but in my sample it turns out to be positively correlated.
I probably wouldn't care about it that much if I just wanted to do a one-time analysis, and just wouldn't include this variable in the model. But is there any reasonable way how to include it so that it is only "used" when sample data also confirms that it is negatively correlated?