Logistic Regressions

Steph Locke

2017-04-20

Welcome!

Today’s aim

Understand what a logistic regression is, how to prepare data for a logistic regression, and how to evaluate a model.

About Me

Locke Data

Founded by Steph Locke (that’s me!), Locke Data is a data science consultancy focused on helping organisations get the most out of their data science resources. While we’re happy to do data science projects for you, we’d really like to set you up to do them yourself!

Locke Data offers a broad range of services including:

  • Data Science Readiness Reviews
  • Training and mentoring programmes
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  • Recruitment assistance

If you’d like more information about our services please get in touch via our website, itsalocke.com.

Steph Locke

I am a Microsoft Data Platform MVP with a decade of business intelligence and data science experience.

Having worked in a variety of industries – including finance, utilities, insurance, and cyber-security – I’ve tackled a wide range of business challenges with data.

However, I’m probably best known for my community activities; including presenting, training, blogging and speaking on panels and webinars.

If you have any questions about today’s session, community activity, or data science in general, please get in touch via Locke Data, or my Twitter, @SteffLocke

Logistic regression theory

What is a logistic regression?

A logistic regression is a linear regression, applied to categorical outcomes by using a transformation function.

A linear regression

A linear regression uses a line of best fit (the old \(y = mx + c\)) over multiple variables to predict a continuous variable.

Why do we need a transformation function?

If you’re trying to predict whether someone survives (1) or dies (0), does it make sense to say they’re -0.2 alive, 0.5 alive, or 1.1 alive?