Logistic Regression Quiz

Question 1: What is logistic regression used for?

Classification
Regression
Both
None of the above

Explanation: Logistic regression is primarily used for classification problems where the dependent variable is categorical. It models the probability that an instance belongs to a particular category. (Reference: "Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani)

Question 2: Which function is used in logistic regression?

Sigmoid function
Linear function
Exponential function
Logarithmic function

Explanation: Logistic regression uses the sigmoid function, also known as the logistic function, to map any real-valued number into a value between 0 and 1. This is essential for modeling probabilities. (Reference: "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani)

Question 3: What is the purpose of the odds ratio in logistic regression?

To measure the probability of success
To compare the odds of success to the odds of failure
To calculate accuracy
To maximize the likelihood

Explanation: The odds ratio in logistic regression measures the odds of success relative to the odds of failure. It helps interpret the impact of predictor variables on the response variable. (Reference: "Applied Logistic Regression" by David W. Hosmer Jr., Stanley Lemeshow, and Rodney X. Sturdivant)

Question 4: What is the purpose of regularization in logistic regression?

To increase overfitting
To reduce model complexity
To add noise to the data
To increase bias

Explanation: Regularization in logistic regression is used to prevent overfitting by penalizing large coefficients. It helps create a simpler model that generalizes well to new data. (Reference: "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani)

Question 5: What is multicollinearity in the context of logistic regression?

It refers to the presence of outliers in the data
It indicates a high correlation among independent variables
It is a measure of model complexity
It is a type of regularization technique

Explanation: Multicollinearity in logistic regression occurs when two or more independent variables are highly correlated, making it challenging to isolate their individual effects on the dependent variable. (Reference: "Applied Logistic Regression" by David W. Hosmer Jr., Stanley Lemeshow, and Rodney X. Sturdivant)

Question 6: What is the purpose of the log-odds in logistic regression?

To measure the probability of success
To linearly transform probabilities
To increase model complexity
To calculate accuracy

Explanation: The log-odds in logistic regression are used to linearly transform probabilities, making it possible to model the relationship between predictors and the log-odds of the response variable. (Reference: "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani)

Question 7: How does logistic regression handle outliers?

It ignores outliers during training
It is highly sensitive to outliers
It robustly handles outliers
It removes outliers from the dataset

Explanation: Logistic regression is sensitive to outliers, and their impact can influence the model's performance. Preprocessing techniques or using robust logistic regression can be applied to handle outliers. (Reference: "Applied Logistic Regression" by David W. Hosmer Jr., Stanley Lemeshow, and Rodney X. Sturdivant)

Question 8: What is the purpose of the link function in logistic regression?

To calculate odds ratios
To link predictors to the response variable
To increase model complexity
To calculate accuracy

Explanation: The link function in logistic regression links the linear combination of predictors to the probability of the response variable, transforming the linear equation to the probability scale. (Reference: "Applied Logistic Regression" by David W. Hosmer Jr., Stanley Lemeshow, and Rodney X. Sturdivant)

Question 9: What is the purpose of feature scaling in logistic regression?

To increase model complexity
To normalize the range of independent variables
To remove outliers from the dataset
To calculate accuracy

Explanation: Feature scaling in logistic regression is used to normalize the range of independent variables, ensuring that each variable contributes proportionally to the model. It helps prevent certain features from dominating the others. (Reference: "Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow" by Aurélien Géron)

Question 10: What is the purpose of cross-validation in logistic regression?

To increase model complexity
To evaluate the model's performance on new data
To add noise to the data
To calculate accuracy

Explanation: Cross-validation in logistic regression is used to evaluate the model's performance on new data by partitioning the dataset into training and testing sets multiple times. It provides a more reliable estimate of the model's generalization performance. (Reference: "An Introduction to Statistical Learning" by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani)