full code for this instance at the underside of this post.
Multiple regression is used when your response variable Y is continuous and you may have at the least k covariates, or independent variables...
That is the (and certain last) a part of a Linear Programming series I’ve been writing. With the core concepts covered by the prior articles, this text focuses on goal programming which is...
1. Introduction
It’s pretty clear that the majority of our work shall be automated by AI in the longer term. This shall be possible because many researchers and professionals are working hard to make their...
Full explanation on Linear Regression and the way it learnsEventually we arrived to a fairly good model. The true values I used to generate those numbers were and after only 50 iterations, the...
Some weeks ago, I published a post on LinkedIn.The post was based on the next figure, comparing the predictions made by two models: Linear Regression, and CatBoost.This began a discussion, and I discovered the...
A workflow and code walkthrough for constructing a Bayesian regression model in STANNote: Try my previous article for a practical discussion on why Bayesian modeling could also be the appropriate selection in your task.This...
Part 3: The algorithm under the hoodUp until now, this series has covered the fundamentals of linear programming. In this text, we're going to move from basic concepts into the main points under the...
Part 1 - Basic Concepts and ExamplesLinear programming is a strong optimization technique that's used to enhance decision making in lots of domains. That is the primary part in a multi-part series that may...