30+ Logistic Regression Interview Questions & Answers

Last updated: 29th Nov, 2023

This page lists down the practice tests / interview questions and answers for Logistic regression in machine learning. Those wanting to test their machine learning knowledge in relation with logistic regression would find these practice tests very useful. The goal for these practice tests is to help you check your knowledge in logistic regression machine learning models from time-to-time. More importantly, when you are preparing for interviews, these practice tests are intended to be handy enough. Those going for freshers / intern interviews in the area of machine learning would also find these practice tests / interview questions to be very helpful.

These test primarily focus on following concepts related with logistic regression:

• Types of logistic regression (Binomial, Multinomial, Ordinal)
• Logistic function, logit transformation
• Evaluation of logistic regression (AIC, Deviance calculations)
• Classification problems examples where logistic regression can be applied

Logistic Regression Concepts 

• Logistic Regression for Discrete Outcomes: Logistic regression is used to estimate/predict the discrete-valued output such as success or failure, 0 or 1, etc.
• Binary and Multinomial Classification: Logistic regression can be used for binary classification as well multinomial classification – classifying data in multiple classes.
• Softmax Classifier: In logistic regression, the term “softmax classifier” specifically refers to the multinomial logistic regression, where the softmax function is used to handle multiple classes.  You may want to check out my post on What’s Softmax function and why do we need it?
• Gradient Descent and Cross-Entropy Loss: Logistic regression classifier is trained by applying gradient descent on the cross-entropy loss function. In other words, the weights of the logistic regression classifier are learned using gradient descent algorithm and cross-entropy loss function. You may want to check my post on Cross-entropy loss explained with Python examples.
• Cost Function and Log Loss: The cost function of logistic regression is derived from taking the log of the maximum likelihood function and applying negative to log loss function in order to use gradient descent for optimization purposes. This is why the cross-entropy loss function is also called a log loss function.
• Types of logistic regression model 
  • Binomial Logistic Regression: Used when the dependent variable has two possible outcomes, like ‘success’ or ‘failure’.
  • Multinomial Logistic Regression: Applies when the outcome variable has more than two unordered categories, like ‘type of fruit’.
  • Ordinal Logistic Regression: Suitable for dependent variables with ordered categories, such as ‘satisfaction rating’.
• Logistic regression model is evaluated using some of the following:
  • AIC (Akaike Information Criterion): AIC is a statistical measure used to compare different statistical models. It balances model complexity against goodness of fit. Lower AIC values indicate a better model, considering both the likelihood of the model and the number of parameters used. It helps in model selection.
  • Deviance (Null and Residual): In statistical modeling, deviance measures the difference between a fitted model and a saturated model. Null deviance shows this difference for a model with only the intercept, reflecting the model’s fit if no predictors are used. Residual deviance shows the difference after fitting the model with predictors.
  • ROC Curve (Receiver Operating Characteristic Curve): The ROC curve is a graphical representation used in binary classification to assess the performance of a model. It plots the true positive rate against the false positive rate at various threshold settings. The area under the curve (AUC) indicates the model’s accuracy.
  • Hosmer-Lemeshow Test: This test is used to evaluate the goodness of fit for logistic regression models. It compares observed event rates with predicted probabilities in subgroups of the dataset, providing a measure of how well the model predicts outcomes. A high p-value suggests a good fit.
  • Pseudo R-squared: Pseudo R-squared values are used in logistic regression as counterparts to R-squared in linear regression. They provide an indication of the model’s explanatory power. Different versions (McFadden, Cox and Snell, etc.) measure this in slightly different ways, none of which can be interpreted as the proportion of variance explained as in linear regression.

In case you have not scored good enough, it may be good idea to go through basic machine learning concepts in relation with logistic regression. Following is the list of some my related blog pages:

• Logistic regression
• Understanding AUC-ROC curve

Practice Test / Interview Questions & Answers

 

#1. ______ value of deviance represents the better fit of model

Higher

Lower

#2. Logistic regression is _________ when the observed outcome of dependent variable can have only two values such as 0 and 1 or success and failure

Multinomial

Binary

Ordinal

#3. Logistic regression is _________ when the observed outcome of dependent variable are ordered

Ordinal

Binomial

Multinomial

#4. Logistic regression is _________ when the observed outcome of dependent variable can have multiple possible types

Multinomial

Binomial

Ordinal

#5. ________ regression can be termed as a special case of _________ regression when the outcome variable is categorical

Linear, Logistic

Logistic, Linear

#6. In the context of logistic regression, what does the term “odds ratio” refer to?

The ratio of the number of outcomes to the number of non-outcomes

The ratio of the probabilities of two independent events

The ratio of the odds of an event occurring to the odds of it not occurring

#7. Which of the following is analogous to R-Squared for logistic regression?

All of the above

McFadden R-squared

Cox and Snell R-Squared

Likelihood ratio R-squared

#8. Which of the following can be used to evaluate the performance of logistic regression model?

AIC

Adjusted R-Squared

#9. ______ the value of AUC, better is the prediction power of the model.

Higher

Lower

#10. Given two model with different AIC value, which one would be preferred model?

One with higher AIC value

One with lower AIC value

#11. Which of the following metrics is equal to True Positive / (True positive + False Negative)

Recall

Precision

#12. Deviance is is a function of _________

Logrithmic function of likelihood ratio

Exponential function of likelihood ratio

#13. Which of the following is used to identify the best threshold for separating positive and negative classes?

ROC Curve

Hosmer-Lemeshow test

Both of the above

#14. In a logistic regression model, if the odds ratio for a predictor variable is less than 1, this suggests that:

The predictor has no association with the outcome.

The predictor is negatively associated with the outcome.

The predictor is positively associated with the outcome.

#15. Whether a student will pass or fail in the competitive exam based on hours of study can be solved using _________ regression model

Linear

Logistic

#16. Which of the following tests can be used to assess whether the logistic regression model is well calibrated?

ROC Curve

Both of the above

Hosmer-Lemeshow test

#17. Deviance can be shown to follow __________

Chi-square distribution

F-distribution

t-distribution

None of the above

#18. Which of the following is link function in logistic regression?

Logit

Identity

#19. The odds of the dependent variable equaling a case (given some linear combination x of the predictors) is equivalent to _______

Exponential function of the linear regression function

Log function of the linear regression expression

#20. How much marks a student can get in a competitive exam based on hours of study can be solved using _________ regression model

Linear

Logistic

#21. In the context of logistic regression, if a model has a deviance much larger than its degrees of freedom, this typically indicates:

The model is underfitting.

The model is overfitting.

The model has perfect fit.

#22. Regression coefficients in logistic regression are estimated using ________

Maximum likelihood estimation method

Ordinary least squares method

#23. If the model deviance is significantly ________ than the null deviance, then one can conclude that the predictor or set of predictors significantly improved model fit.

Smaller

Higher

#24. In the context of model comparison, if the difference in deviance between a more complex model and a simpler model is statistically significant, this suggests:

The more complex model does not provide a significantly better fit than the simpler model.

Both models are equally preferable.

The simpler model is preferred due to its lower complexity.

The more complex model provides a significantly better fit than the simpler model.

#25. Logistic Regression uses Softmax function for which of the following?

Binomial

Multinomial

#26. An AUC-ROC value of 0.5 indicates that the model’s ability to discriminate between positive and negative classes is:

Poor

No better than random guessing.

Excellent

Good

#27. Estimation in logistic regression chooses the parameters that ___________ the likelihood of observing the sample values.

Maximizes

Minimizes

#28. Which of the following can be used to evaluate the performance of logistic regression model?

Both of the above

Null and Residual Deviance

AIC

None of the above

#29. ROC curve is a plot of __________ vs ___________

1-specificity, 1-sensitivity

Sensitivity, 1-specificity

#30. Which of the following is an application of logistic regression?

Predicting stock prices

Forecasting sales in retail

Classifying emails as spam or not spam

#31. Which of the following metrics is equal to True Positive / (True positive + False Positive)

Recall

Precision

#32. When using logistic regression for a binary classification problem, if the output of the model is 0.75 for a given observation, this means:

There is a 75% probability of the observation belonging to the positive class.

The model is 75% confident in its prediction.

There is a 25% probability of the observation belonging to the positive class.

#33. ROC related with ROC curve stands for _______

Receiver Operating Characteristic

Regression Operating Characteristic

Regression Optimization Characteristic

#34. If the ROC curve of a model is closer to the top-left corner of the plot, it indicates:

Higher true positive rate and lower false positive rate.

Lower true positive rate and higher false positive rate.

The model has perfect classification.

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Ajitesh Kumar

I have been recently working in the area of Data analytics including Data Science and Machine Learning / Deep Learning. I am also passionate about different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia, etc, and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data, etc. I would love to connect with you on Linkedin.
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