Logistic regression is a classification method used when the Response column is categorical with only two possible values. The probability of the possible outcomes is modeled with a logistic transformation as a weighted sum of the Predictor columns. The weights or regression coefficients are selected to maximize the likelihood of the observed data.
Any Predictor column with character data is expanded into a set of indicator columns: one column for each unique value in the character column. The indicator column for a character value is one if the corresponding entry in the original column contains that character value; otherwise, it is zero. Character data columns used as predictors should each have small numbers of unique values relative to the total number of rows in the data set.
See also:
Details on Classification Modeling – General