MCQs

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Overall MCQs for Reference

1. What kind of algorithm is logistic regression?
   a. Cost function minimization
   b. Ranking
   c. Regression
   d. Classification
   Answer: d. Classificationsanfoundry​
2. What is the primary type of problem solved by logistic regression?
   a. Regression problems
   b. Clustering problems
   c. Classification problems
   d. Association problems
   Answer: c. Classification problemsgeeksforgeeks​
3. What is the output of a logistic regression model?
   a. Continuous values
   b. Probability values between 0 and 1
   c. Categorical labels directly
   d. Clusters
   Answer: b. Probability values between 0 and 1geeksforgeeks​
4. Which activation function is used in classical logistic regression?
   a. Hyperbolic tangent
   b. Rectified Linear Unit
   c. Sigmoid
   d. Linear
   Answer: c. Sigmoidgeeksforgeeks​
5. Which method is used to fit parameters in logistic regression?
   a. Least Squares
   b. Maximum Likelihood
   c. Hinge Loss
   d. Entropy Minimization
   Answer: b. Maximum Likelihoodscribd+1​
6. Which of the following metrics is most suitable for evaluating logistic regression models?
   a. Mean squared error
   b. Accuracy
   c. Adjusted R2
   d. Explained variance
   Answer: b. Accuracygyansetu​
7. What does the logit function in logistic regression represent?
   a. Linear output
   b. Odds of dependent variable
   c. Log-odds of probability
   d. Classification error
   Answer: c. Log-odds of probabilitygithub+1​
8. In logistic regression, what is the key assumption about the relationship between independent variables and the target?
   a. Linear relationship
   b. Homoscedasticity
   c. Linearity between predictors and log-odds
   d. Normal distribution of errors
   Answer: c. Linearity between predictors and log-oddsstudocu​
9. What type of variable does logistic regression predict?
   a. Continuous
   b. Categorical
   c. Ordinal
   d. Interval
   Answer: b. Categoricalsanfoundry​
10. Which type of logistic regression is used for more than two outcome classes?
    a. Binomial
    b. Multinomial
    c. Ordinal
    d. Poisson
    Answer: b. Multinomialgeeksforgeeks​
11. What is a typical loss function used in binary logistic regression?
    a. Mean squared error
    b. Log-likelihood
    c. Hamming loss
    d. Margin loss
    Answer: b. Log-likelihoodmath.arizona+1​
12. Which algorithm is best for a binary classification problem?
    a. Linear Regression
    b. Logistic Regression
    c. K-means Clustering
    d. Principal Component Analysis
    Answer: b. Logistic Regressiongyansetu​
13. What does ROC curve stand for in logistic regression analysis?
    a. Regression Output Curve
    b. Receiver Operating Characteristic
    c. Relative Output Characteristic
    d. Regression Optimization Chart
    Answer: b. Receiver Operating Characteristicscribd​
14. The range of output values of the sigmoid function is:
    a. (-∞, ∞)
    b.sanfoundry​
    c. (-1, 1)
    d. [0, ∞)
    Answer: b.developers.google+2​
15. What method is generally used for regularization in logistic regression?
    a. Lasso
    b. Ridge
    c. Both a and b
    d. Decision Tree Pruning
    Answer: c. Both a and bprojectpro+1​
16. Which value of p in the Lp norm does Ridge regression use for regularization?
    a. 1
    b. 2
    c. 0.5
    d. Infinity
    Answer: b. 2github​
17. What is overfitting in logistic regression primarily caused by?
    a. Too few data points
    b. Too few predictor variables
    c. Too many predictor variables
    d. Too large regularization
    Answer: c. Too many predictor variablesupgrad+1​
18. What method is not applicable for categorical response variables?
    a. Logistic Regression
    b. Linear Regression
    c. Decision Tree
    d. Naive Bayes
    Answer: b. Linear Regressiongeeksforgeeks​
19. Which technique can detect multicollinearity in logistic regression?
    a. VIF (Variance Inflation Factor)
    b. K-means clustering
    c. Silhouette Score
    d. Adjusted R2
    Answer: a. VIF (Variance Inflation Factor)upgrad​
20. Which of the following is true about the odds in logistic regression?
    a. Odds are always less than 1
    b. Odds are always greater than 1
    c. Odds can take any positive value
    d. Odds are negative values
    Answer: c. Odds can take any positive valuegeeksforgeeks+1​
21. Which statistic measures model discrimination quality in logistic regression?
    a. R-squared
    b. AUC (Area Under ROC Curve)
    c. RMSE
    d. Silhouette score
    Answer: b. AUC (Area Under ROC Curve)scribd​
22. What does a high p-value mean for a predictor in logistic regression?
    a. Predictor is significant
    b. Predictor is not significant
    c. Predictor has a large effect size
    d. Predictor is a categorical variable
    Answer: b. Predictor is not significantmath.arizona+1​
23. What is the primary goal of regularization in logistic regression?
    a. To increase variance
    b. To avoid overfitting
    c. To maximize the cost function
    d. To decrease bias
    Answer: b. To avoid overfittingprojectpro+1​
24. Which of the following is not a typical assumption of logistic regression?
    a. No multicollinearity
    b. Independence of observations
    c. Homoscedasticity
    d. Linear relation between log-odds and predictors
    Answer: c. Homoscedasticitygithub​
25. What type of solver is commonly used to optimize logistic regression models?
    a. Gradient Descent
    b. Simple Linear Regression
    c. K-means
    d. Expectation Maximization
    Answer: a. Gradient Descentmath.arizona+1​
26. What does the term ‘logistic’ in logistic regression refer to?
    a. Loss function
    b. Link function (sigmoid/logit)
    c. Cost function
    d. Algorithm type
    Answer: b. Link function (sigmoid/logit)geeksforgeeks+1​
27. Which library has the function ‘glm()’ for logistic regression?
    a. Python Scikit-learn
    b. R
    c. MATLAB
    d. Java
    Answer: b. Rmath.arizona​
28. For a binary outcome, what values does the target variable take in logistic regression?
    a. 1 or 2
    b. 0 or 1
    c. -1 or 1
    d. Any integer
    Answer: b. 0 or 1sanfoundry+1​
29. What does the likelihood function in logistic regression estimate?
    a. Regression coefficients
    b. Odds ratios
    c. Output probabilities
    d. All of the above
    Answer: d. All of the abovegithub+1​
30. Which function is maximized during the training of logistic regression?
    a. Cost function
    b. Cross-entropy
    c. Likelihood function
    d. Variance
    Answer: c. Likelihood functionscribd+1​
31. What does a confusion matrix display for a logistic regression model?
    a. Feature coefficients
    b. Prediction accuracy by each class
    c. ROC curve
    d. Log-likelihood values
    Answer: b. Prediction accuracy by each classscribd​
32. What happens if regularization parameter is too large in logistic regression?
    a. Model overfits
    b. Model underfits
    c. Model accuracy increases
    d. Loss function increases
    Answer: b. Model underfitsupgrad​
33. Regularization typically has what effect on logistic regression coefficients?
    a. Increases
    b. Sets to zero (L1), reduces (L2)
    c. No effect
    d. Makes them negative
    Answer: b. Sets to zero (L1), reduces (L2)github​
34. Ordinal logistic regression predicts:
    a. Multiple continuous outputs
    b. Multiple unordered categories
    c. Ordered categories
    d. Only binary outputs
    Answer: c. Ordered categoriesgeeksforgeeks​
35. Which value is never output by the sigmoid function?
    a. 0
    b. 1
    c. Infinite
    d. Both a and b
    Answer: c. Infinitedevelopers.google​
36. Which of the following is NOT a performance metric for logistic regression?
    a. Precision
    b. Recall
    c. F1-score
    d. Mean Squared Error
    Answer: d. Mean Squared Errorgyansetu​
37. What is the threshold value usually used in binary logistic regression to assign classes?
    a. 0.9
    b. 0.5
    c. 0.75
    d. 0.25
    Answer: b. 0.5geeksforgeeks​
38. Which statement is TRUE about logistic regression?
    a. Logistic regression can only be used for continuous targets
    b. Logistic regression is non-parametric
    c. Logistic regression estimates probability using the sigmoid function
    d. Logistic regression cannot be regularized
    Answer: c. Logistic regression estimates probability using the sigmoid functiongeeksforgeeks​
39. Which method can help combat multicollinearity in logistic regression?
    a. Increase sample size
    b. Principal Component Analysis
    c. Decrease number of predictors
    d. Both b and c
    Answer: d. Both b and cupgrad​
40. What is meant by the odds ratio in logistic regression?
    a. Probability of event
    b. Probability of non-event
    c. Ratio of probability of event to non-event
    d. Logarithm of probability
    Answer: c. Ratio of probability of event to non-eventsanfoundry+1​
41. Logistic regression is part of which family of models?
    a. Decision trees
    b. Generalized linear models
    c. Bayesian networks
    d. Survival analysis
    Answer: b. Generalized linear modelsmath.arizona+1​
42. When PCA is applied before logistic regression, it serves to:
    a. Normalize outputs
    b. Reduce feature dimensionality
    c. Add more features
    d. Increase overfitting
    Answer: b. Reduce feature dimensionalityupgrad​
43. Which value is sought by maximizing the log-likelihood function?
    a. Best-fit weights (coefficients)
    b. Minimum loss
    c. Median predictor value
    d. Zero error
    Answer: a. Best-fit weights (coefficients)github+1​
44. In logistic regression, which phenomenon can occur due to highly correlated predictors?
    a. Multicollinearity
    b. Overfitting
    c. Underfitting
    d. Class imbalance
    Answer: a. Multicollinearityupgrad​
45. What approach is sometimes used for multi-class problems in logistic regression?
    a. One-vs-One
    b. One-vs-All
    c. Both a and b
    d. Principal Components Regression
    Answer: c. Both a and bscribd​
46. What does L1 regularization (Lasso) do to coefficients?
    a. Shrinks all coefficients equally
    b. Shrinks some coefficients to zero
    c. Squares all coefficients
    d. Sets all coefficients to one
    Answer: b. Shrinks some coefficients to zerogithub​
47. Which kind of logistic regression should be used for ordered classes?
    a. Multinomial
    b. Binomial
    c. Ordinal
    d. Poisson
    Answer: c. Ordinalgeeksforgeeks​
48. Logistic regression can be considered as a special case of:
    a. Support Vector Machine
    b. Neural Networks
    c. Generalized Linear Model
    d. Random Forest
    Answer: c. Generalized Linear Modelgeeksforgeeks​
49. Which scenario would NOT be appropriate for logistic regression?
    a. Binary outcome prediction
    b. Probability estimation
    c. Multiclass classification (with multinomial extension)
    d. Predicting housing prices
    Answer: d. Predicting housing pricesgeeksforgeeks​
50. Which major programming languages/libraries provide built-in logistic regression implementation?
    a. R
    b. Python (scikit-learn)
    c. Both a and b
    d. Neither
    Answer: c. Both a and bmath.arizona+1​

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17. https://in.indeed.com/career-advice/interviewing/logistic-regression-interview-questions
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