Getting ready

In classification, the target variable is one of several categories, and there must be more than one instance of every category. In regression, there can be only one instance of every target variable, as the only requirement is that the target is a real number.

In the case of logistic regression, we saw previously that the algorithm first performs a regression and estimates a real number for the target. Then the target class is estimated by using thresholds. In scikit-learn, there are predict_proba methods that yield probabilistic estimates, which relate regression-like real number estimates with classification classes in the style of logistic regression.

Any regression can be turned into classification by using thresholds. A binary classification can be viewed as a regression problem by using a regressor. The target variables produced will be real numbers, not the original class variables.