Mastering pandas - Second Edition

Logistic regression

In logistic regression, we attempt to predict the outcome of a categorical, that is, discrete-valued dependent variable based on one or more input predictor variables.

Logistic regression can be thought of as the equivalent of applying linear regression but to discrete or categorical variables. However, in the case of binary logistic regression (which applies to the Titanic problem), the function to which we're trying to fit is not a linear one as we're trying to predict an outcome that can take only two values—0 and 1. Using a linear function for our regression doesn't make sense as the output cannot take values between 0 and 1. Ideally, what we need to model for the regression of a binary valued output is some sort of ...