Blog_Banner_Asset
    Homebreadcumb forward arrow iconBlogbreadcumb forward arrow iconArtificial Intelligences USbreadcumb forward arrow iconBinary Logistic Regression: Overview, Capabilities, and Assumptions

Binary Logistic Regression: Overview, Capabilities, and Assumptions

Last updated:
5th Oct, 2021
Views
Read Time
4 Mins
share image icon
In this article
Chevron in toc
View All
Binary Logistic Regression: Overview, Capabilities, and Assumptions

One of the most accepted definitions of Machine Learning goes something like this: 

“A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”

Now, to improve the machine’s performance over time on the same class of tasks, different algorithms are used to optimize the machine’s output and bring it closer to the desired outcomes. Regression Analysis is one of the basic and most used techniques to get the machine to improve its performance.

It consists of an array of machine learning techniques to predict a continuous output variable based on one or more predictor variables’ values. Regression Analysis aims to develop a mathematical equation that can define the continuous variable as a function of the predictor variable. 

Ads of upGrad blog

In one of our earlier articles, we looked at Logistic Regression and how to implement Logistic Regression in Python. We also talked briefly about the three different kinds of Logistic Regressions in Machine Learning. In this article, let’s give you a slightly detailed walkthrough of Binary Logistic Regression along with its overview, capabilities, and assumptions. 

 

Overview of Binary Logistic Regression

Binary or Binomial Logistic Regression can be understood as the type of Logistic Regression that deals with scenarios wherein the observed outcomes for dependent variables can be only in binary, i.e., it can have only two possible types. These two types of classes could be 0 or 1, pass or fail, dead or alive, win or lose, and so on.

Multinomial Logistic Regression works in scenarios where the outcome can have more than two possible types – illness A vs illness B vs illness C – that are not in any particular order. Yet another type of Logistic Regression is Ordinal Logistic Regression that deals with dependent variables in an ordered manner. 

In Binary Logistic Regression, the possible outputs are generally defined as 0 or 1 as this results in the most straightforward interpretation and understanding of the regression model. If a particular outcome for any dependent variable is the successful or noteworthy outcome, it is coded as 0, and if it is unsuccessful or failure, it is coded as 0.

In simple terms, Binary Logistic Regression can be used to carefully and accurately predict the odds of being a case based on the values of the predictors or independent variables. 

Capabilities of Binary Logistic Regression – Types of Questions It Can Answer

As mentioned above, Binary Logistic Regression is ideally suited for scenarios wherein the output can belong to either of the two classes or groups. As a result of that, Binary Logistic Regression is best suited to answer questions of the following nature: 

  • Does the probability of getting cancer change for every additional KG a person is overweight? 
  • Does the said probability vary for every pack of cigarettes smoked per day? 
  • Do bodyweight, fat intake, calorie intake, and age influence the probability of having a heart attack? 

As you can see, the answers to all the above three questions can either be yes or no, 0 or 1. Binary Logistic Regression can therefore be used to precisely answer these questions. 

Major Assumption of Binary Logistic Regression

As with any other Machine Learning algorithm, Binary Logistic Regression, too, works on some assumptions. Here are those: 

  • The dependent variable is dichotomous. That is, it is either present or absent but never both at once. 
  • There should exist no outliers in the data. 
  • There should not be a high correlation or multicollinearity among the different predictors. This can be assessed using a correlation matrix among different predictors. 

In Conclusion

Binary Logistic Regression helps across many Machine Learning use cases. From figuring out loan defaulters to assisting businesses to retain customers – Binary Logistic Regression can be extended to solve even the more complex business problems. However, you should remember that this is just one of the ocean of Machine Learning algorithms techniques. Once you’ve mastered regression analysis, you’re on your way to dealing with more complex and nuanced topics. 

Ads of upGrad blog

If, however, you’re still struggling with making sense of Regression Analysis and beginning your Machine Learning journey, we recommend you our list of Machine Learning courses. At upGrad, we have a learner base in 85+ countries, with 40,000+ paid learners globally, and our programs have impacted more than 500,000 working professionals.

 Our Master of Science in Machine Learning and Artificial intelligence, offered in collaboration with Liverpool John Moores University, is designed to help learners begin from scratch and acquire enough learning to work on real-life projects. Our 360-degree career assistance will ensure that you are fully groomed to take on top roles in the industry. So reach out to us today, and experience the power of peer learning and global networking!

Profile

Pavan Vadapalli

Blog Author
Director of Engineering @ upGrad. Motivated to leverage technology to solve problems. Seasoned leader for startups and fast moving orgs. Working on solving problems of scale and long term technology strategy.
Get Free Consultation

Selectcaret down icon
Select Area of interestcaret down icon
Select Work Experiencecaret down icon
By clicking 'Submit' you Agree to  
UpGrad's Terms & Conditions

Our Best Artificial Intelligence Course

Frequently Asked Questions (FAQs)

1What is the Bayesian statistics model used for?

Bayesian statistical models are based on mathematical procedures and employ the concept of probability to solve statistical problems. They provide evidence for people to rely on new data and make forecasts based on model parameters.

2What is Bayesian Inference?

It is a useful technique in statistics wherein we rely on new data and information to update the probability for a hypothesis using the Bayes' theorem.

3Are Bayesian models unique?

Bayesian models are unique in that all the parameters in a statistical model, whether they are observed or unobserved, are assigned a joint probability distribution.

Explore Free Courses

Suggested Blogs

Top 25 New & Trending Technologies in 2024 You Should Know About
63211
Introduction As someone deeply immersed in the ever-changing landscape of technology, I’ve witnessed firsthand the rapid evolution of trending
Read More

by Rohit Sharma

23 Jan 2024

Basic CNN Architecture: Explaining 5 Layers of Convolutional Neural Network [US]
6375
A CNN (Convolutional Neural Network) is a type of deep learning neural network that uses a combination of convolutional and subsampling layers to lear
Read More

by Pavan Vadapalli

15 Apr 2023

Top 10 Speech Recognition Softwares You Should Know About
5534
What is a Speech Recognition Software? Speech Recognition Software programs are computer programs that interpret human speech and convert it into tex
Read More

by Sriram

26 Feb 2023

Top 16 Artificial Intelligence Project Ideas & Topics for Beginners [2024]
6228
Artificial intelligence controls computers to resemble the decision-making and problem-solving competencies of a human brain. It works on tasks usuall
Read More

by Sriram

26 Feb 2023

15 Interesting Machine Learning Project Ideas For Beginners & Experienced [2024]
5614
Taking on machine learning projects as a beginner is an excellent way to gain hands-on experience and develop a better understanding of the fundamenta
Read More

by Sriram

26 Feb 2023

Explaining 5 Layers of Convolutional Neural Network
5247
A CNN (Convolutional Neural Network) is a type of deep learning neural network that uses a combination of convolutional and subsampling layers to lear
Read More

by Sriram

26 Feb 2023

20 Exciting IoT Project Ideas & Topics in 2024 [For Beginners & Experienced]
10192
IoT (Internet of Things) is a network that houses multiple smart devices connected to one Cloud source. This network can be regulated in several ways
Read More

by Sriram

25 Feb 2023

Why Is Time Complexity Important: Algorithms, Types & Comparison
7671
Time complexity is a measure of the amount of time needed to execute an algorithm. It is a function of the algorithm’s input size and the type o
Read More

by Sriram

25 Feb 2023

Curse of dimensionality in Machine Learning: How to Solve The Curse?
11565
Machine learning can effectively analyze data with several dimensions. However, it becomes complex to develop relevant models as the number of dimensi
Read More

by Sriram

25 Feb 2023

Schedule 1:1 free counsellingTalk to Career Expert
icon
footer sticky close icon