Machine learning has become one of the most soughtafter branches of AI that focuses on algorithms and data to imitate the process of how a human learns and improves accuracy. In ML or machine learning, the most straightforward algorithm is linear regression.
It is a straightforward method for predictive analysis. If a student wants to enroll in a Machine learning and Artificial Intelligence course, they need to learn what linear regression is. Mentioned below is a comprehensive analysis of linear regression.
Regression is the supervised learning methodology that enables the process of discovering correlations among variables. Regression problems arise when an output variable is a continuous or real value. Linear regression showcases the relationship between constant variables. It shows a linear relationship between the Xaxis or independent variables and Yaxis or dependent variables.
Suppose there’s one input variable, i.e., X; it will be simple linear regression. If more than one input variable is present, multiple linear regression will occur.
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To understand everything about linear regression, you first need to get an insight into its importance in ML. In short, it is one of the most important algorithms belonging to supervised ML.
What it does is try applying relations that predict an outcome of the event depending on the independent variables’ data points. This relation happens to be a straight line that fits various data points. Its output is continuous, so it is in a numerical value.
To understand how linear regression works, you need to know its mathematical representation. In mathematics, it can be expressed in the following equation:
y= β0+ β 1x+ ε, where: Y is the dependent variable X is the Independent Variable β 0 is the intercept of that line β1 is the linear regression coefficient (or the line's slope) ε is the random error
Note that the linear regression algorithm shows the linear relationship between y and y (a dependent and one or more independent variables). So, that means it finds the value of the dependent variable changing as per the change in the value of an independent variable. As a matter of fact, the relationship between dependent and independent variables is a straight line with a slope.
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Linear regression is important only due to the fact that it offers a scientific calculation that identifies and predicts future outcomes. Its ability to find predictions and assess them can offer rewarding benefits to individuals and businesses. Linear regression can perform greatly for linearly separable data.
In addition, it is seamless to implement and effective to train. Besides, it also handles overfitting using dimensionally reduction techniques, crossvalidation, and regularisation. The last advantage of linear regression is the extrapolation beyond its specific data set.
If you want to learn about the various types of linear regression and applications, you may note down the following details:
There are majorly two types of linear regression. The first one is the simple linear regression. If one independent variable is used for predicting the numerical value’s dependent variable, it is known as simple linear regression.
Simple linear regression shows the relationship between a dependent variable and an independent variable through a straight line.
A statistical method used for establishing a relationship between two variables via a straight line, simple linear regression has several applications. But first, let’s know how it works. Simple linear regression helps model a relationship between two continuous variables. The prime goal is to anticipate a value of the output variable depending on the input’s value.
Simple linear regression is implemented in the following ways in the practical world. If you wish to learn about them, please get a brief insight into the best linear regression examples:
Lastly, it can help predict the salary of any individual based on their years of experience
SLR is implemented in the following ways:
Among the two types of linear regression, multiple linear regression is the second one. If there’s more than one independent variable, the overall governing linear equation takes another form. Here, the equation is y= c+m1x1+m2x..
It is multiple linear regression, or MLR, where it demonstrates a mathematical relationship among various variables. MLR examines how an independent variable gets correlated to a dependent one.
Multiple linear regression evaluates the relative impacts of independent or explanatory variables on dependent ones. At the same time, it also holds other variables in the model constant. It is different from SLR:
SLR involves just one x and y variable, while MLR involves more than one x and one y variable.
Here’s enlisting the most common realworld linear regression examples.
Here’s how MLR is implemented:
This is a technique used for anticipating the outcome. Let’s understand how it works in the following point:
Polynomial regression is the relationship between independent as well as dependent variables. Here, the dependent variable and independent variable are interconnected with the nth degree.
The polynomial regression model happens to be a machine learning model that captures nonlinear relationships between variables by fitting the nonlinear regression line. It may not be possible with the SLR.
Here’s a brief understanding of the implementation of polynomial regression:
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Logistic regression is a statistical technique employed to understand the association between a binary dependent variable and one or more independent variables. Unlike SLR, which focuses on predicting a continuous outcome, logistic regression is tailored for predicting the probability of an event occurring or not.
How Logistic Regression Works?
Much like SLR, logistic regression aims to model the relationship between variables. However, the key distinction lies in the nature of the dependent variable, which is binary in logistic regression. This binary outcome could be represented as 0 or 1, yes or no, true or false, making logistic regression particularly useful in scenarios where the outcome is categorical.
The logistic regression process involves utilising the logistic function to convert a linear combination of independent variables into a probability score. The logistic function, also known as the sigmoid function, constrains the output to a range between 0 and 1. This probability score is then used to classify observations into different categories.
Ordinal regression is a statistical approach designed to analyse and understand the relationship between an ordinal dependent variable and one or more independent variables. Unlike SLR, which focuses on predicting continuous outcomes, ordinal regression tackles scenarios where the dependent variable is ordered or ranked.
How Ordinal Regression Works?
Similar to SLR, ordinal regression aims to model the relationship between variables, but it is tailored for situations where the outcome variable has inherent order or hierarchy. This hierarchy could include categories like low, medium, high, or any other ordered scale.
The essence of ordinal regression lies in predicting the likelihood of an observation falling into a particular category or order. It utilises cumulative probability functions to estimate the probabilities associated with each category, considering the order and the distance between categories.
Linear regression is the analysis assessing whether one (or more) predictor variables elucidate dependent (criterion) variables. A regression comprises five assumptions, including the following:
Enlisted below are the applications of linear regression:
Let’s explore the key differences between the types of Liner regression on detail:
Differences Based on Parameters  Overfitting  Underfitting 

Definition  It is a common pitfall in deep learning where the model fits training data, memorises data patterns and noise fluctuations. Such models cannot generalise or perform greatly (in case of unseen data, so it defeats the purpose of the model.  The main difference between underfitting and overfitting is that the former, fails to create a mapping between an input and target variable. Here, the model performs greatly in a training set but fails to generalise learning to a testing set.

How to Avoid 
More data training Data augmentation Crossvalidation Data simplification Regularisation and more 
Decrease regularisation Increase trainin duration Removing noise from data 
This post has compiled everything about linear regression in detail starting from its meaning, types, and applications.
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