Hits: 190
Linear Regression in R using Principal Component 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.
Principal Component Regression (PCR) is a variation of linear regression that uses a technique called principal component analysis (PCA) to reduce the number of independent variables. PCA is a method for transforming a set of correlated variables into a smaller set of uncorrelated variables, called principal components.
The idea behind PCR is to use the principal components as independent variables instead of the original independent variables. The principal components are chosen such that they explain the maximum variance in the data. This can help to reduce the number of independent variables, which can improve the performance of linear regression, especially when the original data is high-dimensional and has multicollinearity.
In PCR, we first perform PCA on the independent variables. This will generate a set of principal components, which are then used as independent variables in linear regression.
It’s important to note that PCR is not always better than the regular linear regression, it depends on the data and the problem at hand. Also, PCR assumes that the principal components are linear combinations of the original independent variables.
Another important aspect, PCR is just a linear regression, so it assumes that the relationship between the independent and dependent variables is linear. If the relationship is non-linear, PCR may not provide accurate results.
In this Data Science Recipe, you will learn: Linear Regression in R using Principal Component Regression.
Linear Regression in R using Principal Component Regression
Free Machine Learning & Data Science Coding Tutorials in Python & R for Beginners. Subscribe @ Western Australian Center for Applied Machine Learning & Data Science.
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
Introduction to Applied Machine Learning & Data Science for Beginners, Business Analysts, Students, Researchers and Freelancers with Python & R Codes @ Western Australian Center for Applied Machine Learning & Data Science (WACAMLDS) !!!
Latest end-to-end Learn by Coding Projects (Jupyter Notebooks) in Python and R:
Applied Statistics with R for Beginners and Business Professionals
Data Science and Machine Learning Projects in Python: Tabular Data Analytics
Data Science and Machine Learning Projects in R: Tabular Data Analytics
Python Machine Learning & Data Science Recipes: Learn by Coding