Dear all, I have a general question about running logistic regression with a combination of numeric and binary predictors (independent variables). I'm sorry if it was already asked before, but I couldn't find an answer in the mail archives.
The data set I'm working with looks as follows: The numeric predictors are gene expression values (continuous values ranging from 0 to 20,000) and the binary predictors are gene mutation values (0: gene is not mutated, 1: gene is mutated). The dependent variable is drug response (here: cell line is sensitive or resistant to a drug). Currently I'm ignoring the heterogeneity of the predictors and run the function as follows: lr = LogisticRegression(penalty='l1', C=1).fit(train_xs, train_ys) I was wondering if I need to adjust the model in order to reflect the combination of numeric and binary predictors? Any hints are highly appreciated... Best regards, Felix ------------------------------------------------------------------------------ _______________________________________________ Scikit-learn-general mailing list Scikit-learn-general@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/scikit-learn-general
