30 Logistic Regression Interview Questions & Practice Tests

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This page lists down a set of 30 interview questions on Logistic Regression (machine learning / data science) in form of objective questions and also provides links to a set of three practice tests which would help you test / check your knowledge on ongoing basis. These questions and practice tests is intended to primarily help interns / freshers / beginners to help them brush up their knowledge in logistic regression from time-to-time.  The following is a list of topics covered on this page.

  • Introduction to logistic regression
  • Logistic regression examples
  • Evaluating performance of logistic regression and related techniques including AIC, deviance, ROC etc.
  • Difference between linear and logistic regression

Logistic Regression Practice Tests

This is a set of practice tests (10 questions and answers each) which can be taken to quickly check your concepts on logistic regression. The questions included in these practice tests are listed in later section.

Logistic Regression Interview Question Set

  1. Logistic regression is used to predict _________ valued output?
    • Continuous
    • Categorical
  2. How much marks a strudent can get in a competitive exam based on hours of study can be solved using _________ regression model
    • Multi-linear
    • Logistic
  3. Logistic regression is _________ when the observed outcome of dependent variable can have only two values such as 0 and 1 or success and failure
    • Binomial
    • Multinomial
    • Ordinal
  4. Whether a strudent will pass or fail in the competitive exam based on hours of study can be solved using _________ regression model
    • Multi-linear
    • Logistic
  5. ________ regression can be termed as a special case of _________ regression when the outcome variable is categorical
    • Logistic, Linear
    • Linear, Logistic
  6. In logistic regression, the goal is to predict _________
    • Actual value of outcome dependent variable
    • Odds of outcome dependent variable
  7. Which of the following can be used to evaluate the performance of logistic regression model?
    • Adjusted R-Squared
    • AIC
  8. Which of the following is link function in logistic regression
    • Identity
    • Logit
  9. Logistic regression is _________ when the observed outcome of dependent variable can have multiple possible types
    • Binomial
    • Multinomial
    • Ordinal
  10. In logistic regression, following technique is used to measure the goodness of the fit
    • Sum of squares calculations
    • Deviance calculations
  11. Which of the following can be used to evaluate the performance of logistic regression model?
    • AIC
    • Null and Residual Deviance
    • Both of the above
    • None of the above
  12. Given two model with different AIC value, which one would be preferred model?
    • One with higher AIC value
    • One with lower AIC value
  13. Deviance is a measure of difference between a _______ model and the _________ model
    • saturated, fitted
    • Fitted, saturated
  14. Logistic regression is _________ when the observed outcome of dependent variable are ordered
    • Binomial
    • Multinomial
    • Ordinal
  15. Logit transformation is log of ___________
    • Odds of the event happening for different levels of each independent variable
    • Ratio of odds of the event happening for different levels of each independent variable
  16. Logistic function is _________
    • Dependent variable equalling a given case
    • Probability that dependent variable equals a case
  17. Deviance is is a function of ________
    • Exponential function of likelihood ratio
    • Logrithmic function of likelihood ratio
  18. The odds of the dependent variable equaling a case (given some linear combination x of the predictors) is equivalent to _______
    • Log function of the linear regression expression
    • Exponential function of the linear regression function
  19. Regression coefficients in logistic regression are estimated using ________
    • Ordinary least squares method
    • Maximum likelihood estimation method
  20. _________ is analogous to __________ in linear regression
    • Sum of squares calculations, deviance
    • Deviance, sum of squares calculations
  21. Deviance can be shown to follow __________
    • t-distribution
    • F-distribution
    • Chi-square distribution
    • None of the above
  22. ______ value of deviance represents the better fit of model
    • Higher
    • Lower
  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
    • Larger
  24. Which of the following is analogous to R-Squared for logistic regression
    • Likelihood ration R-squared
    • McFadden R-squared
    • Cox and Snell R-Squared
    • All of the above
  25. Estimation in logistic regression chooses the parameters that ___________ the likelihood of observing the sample values
    • Minimizes
    • Maximizes
  26. Which of the following tests can be used to assess whether the logistic regression model is well calibrated
    • Hosmer-Lemeshow test
    • ROC Curve
    • Both of the above
  27. ROC related with ROC curve stands for _______
    • Regression Optimization Characteristic
    • Regression Operating Characteristic
    • Receiver Operating Characteristic
  28. Which of the following is used to identify the best threshold for separating positive and negative classes
    • Hosmer-Lemeshow test
    • ROC Curve
    • Both of the above
  29. ROC curve is a plot of __________ vs ___________
    • Sensitivity, 1-specificity
    • 1-specificity, Sensitivity
  30. ______ the value of AUC, better is the prediction power of the model
    • Lower
    • Higher
Ajitesh Kumar

Ajitesh Kumar

Ajitesh has been recently working in the area of AI and machine learning. Currently, his research area includes Safe & Quality AI. In addition, he is also passionate about various different technologies including programming languages such as Java/JEE, Javascript and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc.

He has also authored the book, Building Web Apps with Spring 5 and Angular.
Ajitesh Kumar

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