In the world of data and computer programs, the concept of Machine Learning might sound like a tough nut to crack, full of tricky math and complex ideas.
This is why today I want to slow down and check out the basic stuff that makes all this work with a new issue of my MLBasics series.
Today’s agenda is giving our good old Logistic Regression a swanky upgrade.
By default, Logistic Regression is limited to two-class classification problems. However, we often face multiple-class problems.
So let’s dive into the fascinating world of leveling up Logistic Regression to be able to sort things into more than two baskets 👇🏻
In the ML field, Logistic Regression stands as an optimal model for binary classification problems.
It is the trusted path towards decision-making.
However, there’s a big problem with Logistic Regression: It is like a coin toss — heads or tails, A or B.
But what if you have multiple classes?
Logistic regression is not enough to handle a multiple-class classification. Therefore, to perform so, the model needs to be adapted and there are two main options:
- The first simple approach is using multiple Simple Logistic Regression models to identify each one of the classes we want. It is a straightforward solution.
- A second approach is to generate a new model that accepts multiple classes.
So let’s break down both approaches: