On this planet of information and pc applications, the idea of Machine Studying would possibly sound like a troublesome nut to crack, filled with difficult math and complicated concepts.

That is why right now I need to decelerate and take a look at the fundamental stuff that makes all this work with a brand new difficulty of my MLBasics sequence.

At present’s agenda is giving our good previous Logistic Regression a swanky improve.

Why?

By default, Logistic Regression is proscribed to two-class classification issues. Nevertheless, we frequently face multiple-class issues.

So let’s dive into the fascinating world of leveling up Logistic Regression to have the ability to kind issues into greater than two baskets 👇🏻

Within the ML subject, Logistic Regression stands as an optimum mannequin for binary classification issues.

It’s the trusted path in direction of decision-making.

Nevertheless, there’s a giant drawback with Logistic Regression: It is sort of a coin toss — heads or tails, A or B.

However what in case you have a number of lessons?

Logistic regression shouldn’t be sufficient to deal with a multiple-class classification. Due to this fact, to carry out so, the mannequin must be tailored and there are two principal choices:

• The primary easy method is utilizing a number of Easy Logistic Regression fashions to establish every one of many lessons we wish. It’s a simple resolution.
• A second method is to generate a brand new mannequin that accepts a number of lessons.

So let’s break down each approaches: