In this blog post, we’ll be discussing how to perform linear regression in R using the machine learning package caret.

Check out this video for more information:

## Introduction

Linear regression is a statistical approach for modelling relationship between a dependent variable with a given set of independent variables. It is used to estimate real values (continuous) based on linear relationship. It can be extended to multiple linear regression that is used when there are more than one independent variable. The goal of linear regression is to find the best-fitting straight line through the points. The line could be horizontal, vertical or have any other arbitrary slope.

## What is Linear Regression?

Linear regression is a method used to model the relationship between a dependent variable (Y) and one or more independent variables (X). The dependent variable is predicted from the independent variable(s) using a linear equation.

## Why use Linear Regression in Machine Learning?

Linear Regression is a Machine Learning algorithm that is used to predict continuous values. It can be used to predict things like future sales, the price of a stock, or the length of time it takes to complete a task. Linear Regression is a supervised learning algorithm, which means that it needs training data in order to learn. The training data is used to fit a linear model, which can then be used to make predictions. Linear Regression is a relatively simple and fast algorithm, and it is often used as a baseline for more complex algorithms.

## How does Linear Regression Work?

Linear regression is a statistical approach for modeling the relationship between a dependent variable (also known as an outcome variable) and one or more independent variables (also known as predictors). In linear regression, the outcome variable is predicted from a linear combination of the predictor variables. Linear regression can be used to predict both continuous and binary outcome variables.

## Types of Linear Regression

Linear regression is a powerful statistical tool that can be used to predict future values based on historical data. There are several different types of linear regression, and each has its own strengths and weaknesses. In this article, we’ll take a look at the most common types of linear regression and when you might want to use them.

Simple Linear Regression: This type of linear regression is used when there is only one predictor variable. It is the simplest and most straightforward type of linear regression, but it can only be used to make predictions about a single variable.

Multiple Linear Regression: This type of linear regression is used when there are multiple predictor variables. It is more complex than simple linear regression, but it can be used to make predictions about multiple variables.

Polynomial Regression: This type of linear regression is used when the relationship between the predictor variable and the response variable is not linear. Polynomial regression can be used to model non-linear relationships, but it is more complex than simple or multiple linear regression.

## Linear Regression in R

Linear regression is a statistical technique that is used to predict a dependent variable based on one or more independent variables. The linear relationship between the dependent and independent variables is represented by a line, and the line can be used to make predictions about future values of the dependent variable.

Linear regression can be used with any type of data, but it is most commonly used with data that has a linear relationship between the dependent and independent variables. In order to use linear regression, you need to have two columns of data in your dataset: one for the dependent variable (which is the variable that you are trying to predict) and one for the independent variable (which is the variable that you are using to make predictions).

R is a statistical programming language that is widely used for data analysis and statistical computing. R has many built-in functions for performing linear regression, and these functions can be used to fit a linear regression model to your data.

In order to use linear regression in R, you need to first install the following packages:

-stats

-graphics

-grDevices

Once these packages are installed, you can load them into your R session with the following commands:

library(stats)

library(graphics)

library(grDevices)

## Benefits of Linear Regression

Linear regression is one of the most commonly used machine learning algorithms. Linear regression is a supervised learning algorithm used to predict a real-valued output based on a linear combination of input features. Linear regression can be used for both classification and regression tasks.

Linear regression has many benefits over other machine learning algorithms, including:

– Ease of interpretation: The coefficients learned by a linear regression model can be interpreted as the importance of each input feature for predicting the output.

– Ease of implementation: Linear regression is a very simple algorithm to implement and there are many efficient software implementations available.

– Efficiency: Linear regression can be solved efficiently using specialized algorithms, making it practical for large-scale applications.

– Sparsity: When working with high-dimensional data, linear models can be more efficient than other methods because they exploit the sparsity of the data (the number of non-zero features).

## Limitations of Linear Regression

Despite its many advantages, linear regression also has several disadvantages. One disadvantage is that linear regression cannot be used to model non-linear relationships. Another disadvantage is that linear regression is vulnerable to outliers. An outlier is an observation point that is significantly different from the rest of the data. Linear regression tries to find a straight line that best fits all the observation points, so outliers can have a significant impact on the line that is fit.

## Conclusion

To review, we have seen that linear regression is a powerful tool for modeling continuous data. We have also seen that it can be extended to multiple predictor variables, which opens up a whole new world of possibilities for more complex data analysis. While there are many different ways to perform linear regression in R, the caret package provides a simple and streamlined interface that makes it easy to get started.

## References

-Rahul, M. (2018). Machine Learning Linear Regression in R. Retrieved from https://tutorialparadise.com/machine-learning/linear-regression-in-r/

-Saporta, G. (2012). Linear Regression: Least Squares and Maximum Likelihood Estimation.Retrieved from https://stats.idre.ucla.edu/r/dae/fundamentals-of-machine-learning/

Keyword: Machine Learning Linear Regression in R