Logistic Regression Explained

With video explanation | Data Series | Episode 7.1

Logistic Regression can be thought of as an extension of Linear Regression. With Linear Regression our final output for our model took a single value, however, with logistic regression, we apply an extra function to Linear Regression that puts our final value output into a group i.e. 1 or 0.

What is Logistic Regression?

Logistic regression is a very common supervised machine learning algorithm (see Episode 3) used by Data Scientists to categorize data into groups.

Overview

The job of logistic regression is to take a bunch of input data and organise the data into different groups. For example take a look at the following table of weather data gathered from Albury, Australia:

• Our objective is to predict whether it will rain tomorrow in Albury.
• Our model, therefore, has two group outputs: 0 - No or 1 - Yes.
• Since our model has group outputs we use logistic regression to achieve our objective.