6 Logistic Regression Interview Questions And Sample Answers
By Indeed Editorial Team
Published 5 September 2022
The Indeed Editorial Team comprises a diverse and talented team of writers, researchers and subject matter experts equipped with Indeed's data and insights to deliver useful tips to help guide your career journey.
Logistic regression is a statistical classification model used in predictive analytics to estimate the probability of an outcome. When employers interview candidates for logistic regression positions, they may ask about logistic regression concepts and their use for finding binary results. By understanding the questions that interviewers typically ask during a job interview for a logistic regression analyst position, you can practise answering technical questions and improve your chances of getting a job offer. In this article, we provide a list of logistic regression interview questions and sample answers and share tips to help you prepare for your job interview.
6 Logistic Regression Interview Questions With Sample Answers
You can review the following logistic regression interview questions that interviewers might ask during a job interview, along with the sample answers:
1. What is logistic regression?
Many interviewers may start this question to test your basic understanding of logistic regression. In your answer, you can give them a brief definition of logistic regression and explain its purpose. You can also explain how a logistic regression model is helpful in predictive analytics.
Example: 'Logistic regression is a method of statistical analysis that you can apply to a data set to predict a binary outcome based on the information from available data. With the logistic regression model, you can analyse the relationship between multiple independent variables, consider the available historical data and predict the probability of a dependent data variable falling into one of two possible outcomes.
You can use logistic regression with the supervised learning algorithms in machine learning applications to classify input data sets and known historical responses to the data for predicting responses to new incoming data. It can be helpful for making predictions in various fields.'
2. What are the different types of logistic regression?
After asking you what logistic regression is, the interviewer may move on to assess your knowledge of the different types of logistic regression. You can respond by listing the three main types of logistic regression and giving definitions for each. You can also provide examples of each type.
Example: 'The three main types of logistic regression are binary, multinomial and ordinal. Binary logistic regression classifies variables with either/or solutions. This means there can only be two possible outcomes, essentially yes or no. For example, a bank might use logistic regression to decide whether to give a loan to a customer. You can classify an item into three or more predefined classes in the multinomial logistic regression model. For example, a dog owner might use it to determine if their dog prefers fruits, vegetables or biscuits.
The ordinal logistic regression model can also classify an item into multiple classes in predetermined class order. The classes can be disproportionate and have varying distances between them. An example of ordinal logistic regression would be ranking a retailer or a service on a scale of zero to five stars.'
3. How does logistic regression differ from linear regression?
Knowing the difference between logistic regression and linear regression is essential for work purposes for data scientists, the recruiting may ask this question during a job interview to assess your understanding of logistic regression. You can list a few differences between them and explain these briefly.
Example: 'There are several differences between the logistic regression and linear regression models. One major difference is that logistic regression is useful for resolving classification problems, while linear regression is helpful for resolving regression problems. You can predict the value of categorical variables in logistic regression, while you can predict the value of continuous variables in linear regression. You can classify samples by finding the S-shaped sigmoid curve in logistic regression, while you can predict the output by finding the best-fitted line in linear regression.
It is not always necessary for the dependent and independent variables to have a linear relationship in logistic regression, while a linear relationship is necessary between them in linear regression. You can use maximum likelihood estimation to calculate loss function on logistic regression, while you can use mean squared error for the same calculation in linear regression.'
4. How do the softmax and sigmoid functions differ from each other?
It is essential to understand the difference between the softmax function and the sigmoid function clearly to work with neural networks in machine learning. When an interviewer asks this question to assess your understanding, you can first explain each of these functions. You can then list the differences between them.
Example: 'The softmax function, also known as the normalised exponential function or softargmax, it is useful in the multinomial logistic regression model to classify multiple classes when there are mutually exclusive outputs or there can be only one correct answer. The sigmoid function is a mathematical function that forms an S-shaped curve, known as a sigmoid curve, on a graph. It applies to the binary logistic regression model in multi-label classification when there are non-exclusive outputs or more than one correct answer.
You can use the softmax function in the different layers of a neural network, while the sigmoid function is helpful as an activation function when you are building a neural network.'
5. What are the advantages and disadvantages of logistic regression?
This is a commonly asked interview question to test your experience of working with logistic regression. In your answer, you can begin by listing the advantages of logistic regression. You can follow that with the disadvantages of logistic regression.
Example: 'The primary advantage of logistic regression is that it is easy to understand and does not require extensive training to implement and interpret it. The data classification with logistic regression is fast and accurate for unknown records and simple datasets. It performs well with linearly separable datasets.
The logistic regression model usually avoids overfitting, but when it works with high-dimensional datasets, it can learn the details, noise and random fluctuations in the training data as concepts. This can prevent the model from performing well on new data. You can use regularisation techniques to avoid that. Another disadvantage is that the logistic regression model's linear decision surface prevents it from resolving non-linear problems.'
6. What is regularisation and why is it important?
The reason for asking this logistic regression interview question is to find out if you know how to minimise the problem of overfitting in logistic regression. You can answer by defining regularisation and explaining its importance. You can mention how regularisation can improve model performance.
Example: 'Regularisation is a technique that you can use to avoid the problem of overfitting in logistic regression. It is important because it modifies learning algorithms to enable them to generalise better on unseen data and reduce their generalisation errors. The changes to the algorithms do not affect their training errors.'
Tips To Prepare For Logistic Regression Interview Questions
The following tips may help you improve your chances of success in a job interview for a logistic regression position:
Research the company and the logistic regression role
Before the interview, take the time to research the company and the logistic regression position requirements. You can get information on the company through its website, social media accounts and professional networking accounts. You may also learn more about it by reading related news articles and industry reports. It may also help to network with analysts and developers working with logistic regression for the company and find out about their exact work duties. Knowing what the employer expects from you if they hire can help you prepare well for the upcoming interview.
Improve your technical knowledge of logistic regression
As the interviewer is can expect you to be well-informed about logistic regression, it is preferable to refresh your knowledge on the topic before you go for the interview. Interviewers may ask you about your experience with Python, SAS, R or other analytics tools. They may also ask about your experience with segmentation methodology, data wrangling and data analysis. By preparing well, you can increase the chances of convincing the interviewers of your in-depth knowledge and suitability for the role.
List logistic regression work experience
Employers are more likely to consider you for the available job if you can show how you used logistic regression to resolve real-world problems successfully. For example, you might have used it to assess your daily work productivity. If you do not have extensive work experience, create self-initiated logistic regression models on your own to display your expertise.
Develop confidence with mock interviews
Aside from being knowledgeable, you can improve your chances of getting shortlisted by presenting yourself as a calm, confident professional. Ask a family member, friend or colleague to help you prepare with mock interviews and get their feedback on your performance. You can also video record yourself and assess how well you do with your articulation level, tone and body language to help you improve your performance effectively.
Please note that none of the companies, institutions or organisations mentioned in this article are associated with Indeed.
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