Three Simple Things About Regression That Every Data Scientist Should Know

I consider myself more of a mathematician than a data scientist. I can’t bring myself to execute methods blindly, with no understanding of what’s going on under the hood. I have to get deep into the math to trust the results. That’s a good thing because it’s very easy nowadays to just run models and go home. A model is only as good as your understanding of it, and I worry that a lot of people are running models and just accepting the first thing that comes out of them. When it comes to regression modeling — one of the most common forms of modeling out there — you’ll be a better data scientist if you can understand a few simple things about how these models work and why they are set up the way they are. <h2>1. You are predicting an average — not an actual value</h2> When you run a regression model, usually you are finding a relationship between the input variables and some sort of mean value related to the outcome. Let’s look at linear regression. When we run a linear regression we are making two very important assumptions about our outcome variable y: <ol> <li>That the possible values of y for any given input variables are distributed around a mean.</li> <li>That the mean of y has an additive relationship with the input variables. That is, to get the mean of y you add up some numbers that depend on each input variable.</li> </ol> <a href="https://keith-mcnulty.medium.com/three-simple-things-about-regression-that-every-data-scientist-should-know-d38ee17c5563">Click Here</a>