Linear Regression in R using Partial Least Squared Regression

Hits: 112

Linear Regression in R using Partial Least Squared Regression

Linear regression is a statistical method that helps to understand the relationship between a dependent variable and one or more independent variables. It is represented by an equation in the form of Y = a + bX, where Y is the dependent variable, X is the independent variable, a is the y-intercept, and b is the slope of the line. It helps to make predictions about the dependent variable, based on the values of the independent variables.

Partial Least Squared Regression (PLSR) is a variation of linear regression that uses a technique called partial least squares (PLS) to reduce the number of independent variables. PLSR is particularly useful when the number of independent variables is large, and when there is a high degree of correlation between the independent variables, known as multicollinearity.

PLS works by projecting the independent variables onto a new set of variables, called latent variables, that are chosen to explain the maximum variance in the dependent variable. These latent variables are then used as independent variables in linear regression, instead of the original independent variables.

PLSR is a two-step process, first, it performs PLS on the independent variables to obtain latent variables. Second, it performs linear regression using the latent variables as independent variables.

It’s important to note that PLSR is not always better than the regular linear regression, it depends on the data and the problem at hand. Also, PLSR assumes that the relationship between the independent and dependent variables is linear. If the relationship is non-linear, PLSR may not provide accurate results.

 

In this Data Science Recipe, you will learn: Linear Regression in R using Partial Least Squared Regression.



Linear Regression in R using Partial Least Squared Regression

Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.

 

Personal Career & Learning Guide for Data Analyst, Data Engineer and Data Scientist

Applied Machine Learning & Data Science Projects and Coding Recipes for Beginners

A list of FREE programming examples together with eTutorials & eBooks @ SETScholars

95% Discount on “Projects & Recipes, tutorials, ebooks”

Projects and Coding Recipes, eTutorials and eBooks: The best All-in-One resources for Data Analyst, Data Scientist, Machine Learning Engineer and Software Developer

Topics included: Classification, Clustering, Regression, Forecasting, Algorithms, Data Structures, Data Analytics & Data Science, Deep Learning, Machine Learning, Programming Languages and Software Tools & Packages.
(Discount is valid for limited time only)

Disclaimer: The information and code presented within this recipe/tutorial is only for educational and coaching purposes for beginners and developers. Anyone can practice and apply the recipe/tutorial presented here, but the reader is taking full responsibility for his/her actions. The author (content curator) of this recipe (code / program) has made every effort to ensure the accuracy of the information was correct at time of publication. The author (content curator) does not assume and hereby disclaims any liability to any party for any loss, damage, or disruption caused by errors or omissions, whether such errors or omissions result from accident, negligence, or any other cause. The information presented here could also be found in public knowledge domains.

Learn by Coding: v-Tutorials on Applied Machine Learning and Data Science for Beginners

There are 2000+ End-to-End Python & R Notebooks are available to build Professional Portfolio as a Data Scientist and/or Machine Learning Specialist. All Notebooks are only $19.95. We would like to request you to have a look at the website for FREE the end-to-end notebooks, and then decide whether you would like to purchase or not.

Please do not waste your valuable time by watching videos, rather use end-to-end (Python and R) recipes from Professional Data Scientists to practice coding, and land the most demandable jobs in the fields of Predictive analytics & AI (Machine Learning and Data Science).

The objective is to guide the developers & analysts to “Learn how to Code” for Applied AI using end-to-end coding solutions, and unlock the world of opportunities!

 

Linear Regression in R – partial least squares regression in R

 

R tutorials for Business Analyst – Merge Data Frames in R: Full and Partial Match