I have about 8 features as my predictors in a logistic model I am trying to fit in python. One of the features is TotalAward (Financial Aid) and the second is NEED. I am attempting to predict the likelihood of student retention (not leaving/transferring). Since the feature (NEED) affects the relationship between TotalAward and Retention (y=1), I would like to interact it without interacting any other features in my (X=df[[ 'x1', 'x2', etc]]). In other statistical software, like stata, you would simply do $X1*X2$. How can I do this in python?



If you work with pandas, you can simply multiply the two variables:

df['c'] = df.a * df.b

Other sklearn solutions are a little daunting. But with pasty, you can construct design matrixes.


E.g. X, y = dmatrices('y ~ x1 + x2 + x3 + x1:x2', df)

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  • $\begingroup$ Thank you. I assume that the first solution is for two continuous variables. How would you interact a categorical (or binary) with a continuous variable. $\endgroup$ – Scott Sep 29 '19 at 1:55

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