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