Hi , I'm using logistic regression in Enterprise Miner (14.1) to predict a binary outcome. I have a population of about 500,000 and 20 explanatory variables. All explanatory variables are nominal with about 3 to 10 possible values each. Due to the large number of combinations (3 values of var1 * 5 values of var2 * 8 values of var3 * ….) each combination contains very small sub-population sometimes no more than 2 to 5 observations. These small group affect the prediction and the stability of the model. Is there a way to force minimum number of observations in a subgroup (=combination of variable values) in logistic regression (something similar to the minimum observations in a leaf of a decision tree)? How to deal with this problem? Overall the affected number of observations is small but since this model is used for credit scoring this exceptions do raise questions from the sales persons that uses the outcome of the model. Best regards Moshe
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