Introduction to Multiclass Logistic Regression Classification

Logistic regression is one of the most popular and widely used classification algorithms, and by default, it’s limited to a binary class classification problem. However, logistic regression can be used for multiclass classification using extensions like one-vs-rest (OVR) and multinomial.

• In OVR, the problem is first transformed into multiple binary classification problems, and under the hood, separate binary classifiers are trained for all classes.

• In multinomial, the solvers learn an accurate multinomial logistic regression model. In this case, the probability estimates should be better calibrated than OVR. The cross-entropy error/loss function supports multiclass classification problems, such as maximum likelihood estimation.

Let's work with a multiclass classification problem using the extensions mentioned above in logistic regression.

The dataset, exploratory data analysis, and preprocessing

We’ll be working with the very famous Iris dataset. It is used for classifying three flower types based on some given features.