Multiple Linear Regression from Scratch in Numpy
source link: https://www.tuicool.com/articles/ziy6Rvn
Go to the source link to view the article. You can view the picture content, updated content and better typesetting reading experience. If the link is broken, please click the button below to view the snapshot at that time.
Multiple Linear Regression from Scratch in Numpy
Oct 26 ·7min read
Linear regression is probably the most simple ‘machine learning’ algorithm. I bet you’ve used it many times, possibly through Scikit-Learn or any other library providing you with an out-of-the-box solution.
But have you ever asked yourself: How does the model actually work behind the scenes?
Sure, in case of simple linear regression ( only one feature ) you can calculate slope and intercept coefficients with a simple formula, but those formulas cannot be transferred to multiple regression. If you don’t know anything about simple linear regression, check out this article:
Today I will focus only on multiple regression and will show you how to calculate the intercept and as many slope coefficients as you need with some linear algebra. There will be a bit of math, but nothing implemented by hand. You should be familiar with the terms like matrix multiplication, matrix inverse, and matrix transpose.
If those sound like science fiction, fear not, I have you covered once again:
At the bottom of that article is a link to the second part, which covers some basic concepts of matrices. Let’s now quickly dive into the structure of this article:
- Math behind
- Imports
- Class definition
- Declaring helper function
- Declaring fit() function
- Declaring predict() function
- Making predictions
- Conclusion
A lot of stuff to cover, I know. I’ll try to make it as short as possible, and you should hopefully be able to go through the entire article in less than 10 minutes.
Okay, let’s dive in!
Recommend
-
78
code and opinions
-
50
In this post we will see how to include the effect of predictors in non-linear regressions. In other words, letting the parameters of non-linear regressions vary according to some explanatory variables (or predictors). B...
-
35
We’re living in the era of large amounts ofdata, powerful computers, and artificial intelligence. This is just the beginning.Data science and machine learning are driving image recognition, autonomous vehicles developmen...
-
44
Linear Regression is an approach that tries to find a linear relationship between a dependent variable and an independent variable by minimizing the distance as shown below. ...
-
16
Mixed-effects regression models are a powerful to...
-
45
Linear Algebra Essentials with Numpy (part 1)
-
21
Detailing and Building a Linear Regression model from scratch
-
9
Multiple Linear Regression in SASSAS Tutorial | Multiple Linear Regression in SAS - YouTubeSAS UsersSUBSCRIBE
-
8
Tuesday, February 2, 20216:00 PM to 8:00 PM MSTOnline eventLink visible for attendeesDetai...
-
4
Multiple Linear Regression in SQL with Only SUM() and AVG() This post is inspired by someone dropping this in my mentions today:
About Joyk
Aggregate valuable and interesting links.
Joyk means Joy of geeK