# aliquote

## < a quantity that can be divided into another a whole number of time />

Logistic regression is not fundamentally a classification algorithm.

The problem arises from logistic regression often being taught as a “classification” algorithm in the machine learning world. I was personally not taught this way– I learned from econometricians that you can use either probit or “logit” as general linear models in the event you want to estimate on a binary target variable, and that these models calculate probabilities. Thinking about logistic regression as a probability model easily translates to the classification case, but the reverse simply does not seem to be true. — Why Do So Many Practicing Data Scientists Not Understand Logistic Regression?