Machine Learning Quiz 04: Logistic Regression

Let’s check your basic knowledge of Logistic Regression. Here are 10 multiple-choice questions for you and there’s no time limit. Have fun!

Question 1: Logistic regression is used for ___?
(A) classification
(B) regression
(C) clustering
(D) All of these

Question 2: Logistic Regression is a Machine Learning algorithm that is used to predict the probability of a ___?
(A) categorical independent variable
(B) categorical dependent variable.
(C) numerical dependent variable.
(D) numerical independent variable.

Question 3: You are predicting whether an email is spam or not. Based on the features, you obtained an estimated probability to be 0.75. What’s the meaning of this estimated probability? (select two)
(A) there is 25% chance that the email will be spam
(B) there is 75% chance that the email will be spam
(C) there is 75% chance that the email will not be spam
(D) there is 25% chance that the email will not be spam

Question 4: In a logistic regression model, the decision boundary can be ___.
(A) linear
(B) non-linear
(C) both (A) and (B)
(D) none of these

Question 5: What’s the cost function of the logistic regression?
(A) Sigmoid function
(B) Logistic Function
(C) both (A) and (B)
(D) none of these

Question 6: Why cost function which has been used for linear regression can’t be used for logistic regression?
(A) Linear regression uses mean squared error as its cost function. If this is used for logistic regression, then it will be a non-convex function of its parameters. Gradient descent will converge into global minimum only if the function is convex.
(B) Linear regression uses mean squared error as its cost function. If this is used for logistic regression, then it will be a convex function of its parameters. Gradient descent will converge into global minimum only if the function is convex.
(C) Linear regression uses mean squared error as its cost function. If this is used for logistic regression, then it will be a non-convex function of its parameters. Gradient descent will converge into global minimum only if the function is non-convex.
(D) Linear regression uses mean squared error as its cost function. If this is used for logistic regression, then it will be a convex function of its parameters. Gradient descent will converge into global minimum only if the function is non-convex.

Question 7: You are predicting whether an email is spam or not. Based on the features, you obtained an estimated probability to be 0.75. What’s the meaning of this estimated probability? The threshold to differ the classes is 0.5.
(A) The email is not spam
(B) The email is spam
(C) Can’t determine
(D) both (A) and (B)

Question 8: What’s the the hypothesis of logistic regression?
(A) to limit the cost function between 0 and 1
(B) to limit the cost function between -1 and 1
(C) to limit the cost function between -infinity and +infinity
(D) to limit the cost function between 0 and +infinity

Question 9: Which one is not true?
(A) If we take the weighted sum of inputs as the output as we do in Linear Regression, the value can be more than 1 but we want a value between 0 and 1. That’s why Linear Regression can’t be used for classification tasks.

(B) Logistic Regression is a generalized Linear Regression in the sense that we don’t output the weighted sum of inputs directly, but we pass it through a function that can map any real value between 0 and 1.
(C) The value of the sigmoid function always lies between 0 and 1

(D) Logistic Regression is used to determine the value of a continuous dependent variable

Question 10: In a logistic regression, if the predicted logit is 0, what’s the transformed probability?
(A) 0
(B) 1
(C) 0.5
(D) 0.05

The solutions will be published in the next quiz Machine Learning Quiz 05. Happy learning. If you like the questions and enjoy taking the test, leave a clap for me. Feel free to discuss/share your thoughts on these questions in the comment section.

Solution of Machine Learning Quiz 03 are 1(A), 2(C), 3(A), 4(A), 5(C), 6(A), 7(A,C), 8(A), 9(A,D), 10(C,D)

References:

[1] Introduction to Logistic Regression: https://towardsdatascience.com/introduction-to-logistic-regression-66248243c148

[2] Logistic Regression — Detailed Overview: https://towardsdatascience.com/logistic-regression-detailed-overview-46c4da4303bc

[3] Building a Logistic Regression in Python: https://towardsdatascience.com/building-a-logistic-regression-in-python-301d27367c24

More from Md Shahidullah Kawsar

Data Scientist at IDARE in the lone star state.