Linear regression vs logistic regression: difference and working BRAND EQUITY

Linear regression vs logistic regression: difference and working

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Machine learning is one of the majorly advancing technologies in today’s data-driven world. Many businesses survey their audience and gather loads and loads of data to make conclusions out of it. And they use advanced data science tools for the prediction process. 

Linear regressions and logistic regression are the two most famous and commonly used algorithms when it comes to machine learning. Both being supervised machine learning algorithms, they serve different purposes. Linear regression is used for predicting continuous values, whereas logistic regression is used in the binary classification of values. 

In this article, we will have a look at how the two are different from each other. First, let’s begin by defining the two.

Linear Regression Vs Logistic Regression: Definition

What is Linear Regression?

A supervised machine learning algorithm linear regression assumes the presence of a linear relationship between independent and dependent variables. Linear regression is used to predict value based on the independent variable. 

What is Logistic Regression?

Logistic regression is also a supervised machine learning algorithm. However, the point of difference is that it is a classification algorithm. Logistic regression uses the value of the independent variable to predict the category of the dependent variable. 

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Difference between Linear regression and Logistic regression

Feature

Linear regression

Logistic regression 

Definition 

A supervised learning technique for solving regression problems

A supervised learning technique for mainly used for classification problems

Use 

Used for predicting continuous dependent values with the help of independent variables 

Used for binary classification or separation of discreet dependent values with the help of independent variables

Output 

Output can only be continuous values such as age, height, time, price, salary, etc.

The output can only be between 0 and 1.

Graphical output

We find a best fit linear line which will predict the next value or variable

We find a s-curve or sigmoid curve which classify the variables

Estimation accuracy

Least square method

Maximum likelihood estimation method

Variable relationship

Relationship between dependent and independent variable should be linear

Relationship between dependent and independent variable is not required

Collinearity 

Collinearity between independent variables is allowed

Collinearity between independent variables is not allowed

Applications 

Used in businesses and forecasting stocks

Used in classification and image processing

[Related Read: Logistics Regression Assumption]

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Linear Regression Vs Logistic Regression: Working

Steps of performing Linear Regression

Linear regression is a machine learning algorithm used to predict the output variable values based on the input variable values. 

The x variables are the independent input variables and y are the dependent output variables. The value of y variables depends on the value of x variables.

Linear regression vs logistic regression: difference and working BRAND EQUITY

Linear regression works by defining the relationship between input and output variables. It draws a line that plots the input data and maps it to the output data. This line is the line of best fir and is a mathematical representation of the relationship between the independent variables. The line is meant to cover as many input variables as possible and the left out variables are the outliers or noise. 

The regression line is written as:

y= a0 + a1x + e

a0 is t intercept  

a1 is the slope of the line

y is a dependent output variable

x is an independent input variable

e is the error term

 

Example

Linear regression vs logistic regression: difference and working BRAND EQUITY

The above graph shows the experience as the input variable and salary as the output variable. Hence, it means that as your experience grows, your salary is bound to grow too. This way, through linear regression you can predict how much will be your approximate salary when you will have 11 years of experience.

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Steps of performing Logistic Regression

Logistic regression in machine learning is used to predict the category of the dependent variable based on the independent variable with the output as 0 or 1.

The input data already belongs to a category, which means multiple input values can map to one output value. We use logistic regression to predict which category will the new input value belong. Hence the input is mapped into either 0 or 1. 

Linear regression vs logistic regression: difference and working BRAND EQUITY

Once the curve is drawn, showing the data mapping to the output, we need a line to separate these two outputs clearly. This line is called the threshold or the value at which this line is drawn is called the threshold value.

Linear regression vs logistic regression: difference and working BRAND EQUITY

The equation for logistic regression is given by:

Linear regression vs logistic regression: difference and working BRAND EQUITY

Example

Let’s say you have a list of employee IDs and you want to bifurcate the IDs based on legitimate and fraudulent. In such cases, you will use logistic regression.

Similarities between Linear Regression & Logistic Regression

There are very few similarities between the two regression models. 

  1. Both are supervised machine learning algorithms. 
  2. Both the regression model uses the linear equation for prediction. 

Summing up;

This sums up the differences between Linear Regression and Logistic Regression. While linear regression can help you predict the price of a car or an apartment, logistic regression can classify whether a mole in a body is benign or malignant. 

Both the regression model can be used to make informed decisions. To know more about how you can use machine learning to predict outcomes or classify elements you can contact us

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Fast Insights
Best-in-class ROI

Voxco’s platform helps you gather omnichannel feedback, measure sentiment, uncover insights and act on them.

Join 500 + global clients across 40+ countries