Understand what a logistic regression is, how to prepare data for a logistic regression, and how to evaluate a model.
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:
If you’d like more information about our services please get in touch via our website, itsalocke.com.
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
A logistic regression is a linear regression, applied to categorical outcomes by using a transformation function.
A linear regression uses a line of best fit (the old \(y = mx + c\)) over multiple variables to predict a continuous variable.
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?