Logistic regression does not support multiclass classification natively. But, we can use One-Vs-Rest (OVR) or One-Vs-One (OVO) strategy along with logistic regression to solve a multiclass classification problem. As we know, in a multiclass classification problem, the target categorical variable can take more than two different values. And in a binary classification problem, the target categorical variable can take two different values.

We have discussed OVO vs. OVR strategy in detail in this article: One-Vs-Rest (OVR) vs. One-Vs-One (OVO) strategy. Interested readers, please follow the link to read the article.

So, let’s say, the target variable of a multiclass classification problem can take three different values A, B, and C. In the One-Vs-One (OVO) strategy, the multiclass classification problem is broken into the following binary classification problems:

Problem 1: A vs. B
Problem 2: A vs. C
Problem 3: B vs. C

After that, the binary classification problems are solved using a binary classifier. Finally, the results are used to predict the outcome of the target variable.

On the other hand, if we use the One-Vs-Rest (OVR) strategy, then the multiclass classification problem is broken into the following binary classification problems:

Problem 1: A vs. (B, C)
Problem 2: B vs. (A, C)
Problem 3: C vs. (A, B)

Now, the binary classification problems are solved using a binary classifier, and the results are used to predict the outcome of the target variable.

We can solve a multiclass classification problem using logistic regression in the following ways: …