Understanding the Mean Squared Error
If you spend enough time reading about statistics and machine learning, there’s a good chance that you’ll repeatedly encounter the same ideas, terms, and concepts. Some of them start to become more familiar with time and you’ll naturally begin to grasp them with enough repetition.
Occasionally, you’ll find some ideas and definitions that you think you know but haven’t run across them enough to comprehend them. For me, it’s the Mean Squared Error. I learned about Mean Squared Error last semester in a Probability course but didn’t have the context or tools to understand it.
Thankfully, over the course of writing this series, that has all changed. I’ve finally come to understand the Mean Squared Error. And hopefully, by the end of this post, you will too!
The structure of the note
- Mean Square Error (MSE)
- Cost Function
- Calculating MSE in Python
We start with a quick recap from the last note which introduced the concept of linear regression.
In linear regression, we have a training set. Our goal is to learn a function h: X →…
