Quiz Details
QZ-20251220-75936
Topics:
Machine Learning
Lenear regression
logistique regression
Difficulty:
Level 3 - Medium
Questions:
5
Generated:
December 20, 2025 at 03:37 PM
Generated by:
Guest User
Instructions: Select an answer for each question and click "Check Answer" to see if you're correct. Then view the explanation to learn more!
1 What is the primary goal of linear regression?
Correct Answer:
A
Explanation: The primary goal of linear regression is to find a linear relationship between the independent variables and the dependent variable to predict a continuous outcome.
Explanation: The primary goal of linear regression is to find a linear relationship between the independent variables and the dependent variable to predict a continuous outcome.
2 In logistic regression, what is the range of the output probability?
Correct Answer:
B
Explanation: Logistic regression outputs probabilities that range from 0 to 1, representing the likelihood of the dependent variable being in a particular category.
Explanation: Logistic regression outputs probabilities that range from 0 to 1, representing the likelihood of the dependent variable being in a particular category.
3 Which of the following assumptions is NOT required for linear regression?
Correct Answer:
D
Explanation: While linear regression assumes linearity, homogeneity of variance, and independence of errors, it does not require multicollinearity. In fact, multicollinearity can be problematic.
Explanation: While linear regression assumes linearity, homogeneity of variance, and independence of errors, it does not require multicollinearity. In fact, multicollinearity can be problematic.
4 What is the sigmoid function used for in logistic regression?
Correct Answer:
B
Explanation: The sigmoid function is used in logistic regression to convert the linear combination of inputs into a value between 0 and 1, which represents the probability of the target class.
Explanation: The sigmoid function is used in logistic regression to convert the linear combination of inputs into a value between 0 and 1, which represents the probability of the target class.
5 Which metric is commonly used to evaluate the performance of a regression model?
Correct Answer:
D
Explanation: Mean Squared Error (MSE) is commonly used to evaluate the performance of regression models as it measures the average squared difference between the predicted and actual values.
Explanation: Mean Squared Error (MSE) is commonly used to evaluate the performance of regression models as it measures the average squared difference between the predicted and actual values.