LOGISTIC REGRESSION

 

Machine learning generally involves predicting a quantitative outcome or a qualitative class. The former is commonly referred to as a regression problem, and in the case of linear regression, this involves predicting a numeric outcome based on the input of continuous variables. When predicting a qualitative outcome (class), the task is considered a classification problem. Examples of classification problems include predicting what products a user will buy or predicting if a target user will click on an online advertisement (True/False).

Not all algorithms, though, fit cleanly into this simple dichotomy and logistic regression is a notable example. Logistic regression is part of the regression family because, as with linear regression, it involves predicting outcomes based on analyzing quantitative relationships between variables. But unlike linear regression, it accepts both continuous and discrete variables as input and its output is qualitative; it predicts...