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. 

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. 

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!

What 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.

What 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.

Are 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.

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