Logistic Regression MCQ

Test your knowledge with important Logistic Regression MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30+ more Logistic Regression MCQs on Bissoy. Bissoy App

• The output is whether a person will surely vote or surely not vote or may cast a vote, based on one feature. It is an example of multiclass classification.
  ক

  True

  খ

  False
• The output is whether a person will vote or not, based on several features. It is an example of multiclass classification.
  ক

  True

  খ

  False
• When was BFGS invented?
  ক

  1960

  খ

  1965

  গ

  1975

  ঘ

  1970
• Who developed conjugate gradient method?
  ক

  Hestenes and Stiefel

  খ

  Broyden, Fletcher, Goldfarb and Shannon

  গ

  Valiant

  ঘ

  Vapnik and Chervonenkis
• In the L-BFGS algorithm, what does the letter L stand for?
  ক

  Lengthy

  খ

  Limited-memory

  গ

  Linear

  ঘ

  Logistic
• Ax = b => = . Let x0, the initial guess be . What is the residual vector? b) c) d)
  ক

  = . Let x0, the initial guess be . What is the residual vector? ]

  খ

  4 2, 2 3

  গ

  4 2, 2 3

  ঘ

  4 2, 2 3
• Who invented BFGS?
  ক

  Quinlan

  খ

  Bayes

  গ

  Broyden, Fletcher, Goldfarb and Shannon

  ঘ

  Cauchy
• Which is a better algorithm than gradient descent for optimization?
  ক

  Conjugate gradient

  খ

  Cost Function

  গ

  ERM rule

  ঘ

  PAC Learning
• h(x) = 1, y = 0. What is the cost (h(x), y)?
  ক

  -infinite

  খ

  infinite

  গ

  0

  ঘ

  always h(x)
• y = 1. How does cost(h(x), y) change with h(x)?
  ক

  cost(h(x), y) = infinite when h(x) = 1

  খ

  cost(h(x), y) = 0 when h(x) = 0

  গ

  cost(h(x), y) = 0 when h(x) = 1

  ঘ

  it is independent of h(x)
• Let m be the number of training instances. What is the summation of cost function multiplied by to get the gradient descent?
  ক

  1/m

  খ

  m

  গ

  1 + m

  ঘ

  1 – m
• What is the generalized cost function?
  ক

  cost(h(x),y) = -y*log(h(x)) – (1 – y)*log(1-h(x))

  খ

  cost(h(x),y) = – (1 – y)*log(1-h(x))

  গ

  cost(h(x),y) = -y*log(h(x))

  ঘ

  cost(h(x),y) = y*log(h(x)) + (1 – y)*log(1-h(x))
• h(x) = y. What is the cost (h(x), y)?
  ক

  -infinite

  খ

  infinite

  গ

  0

  ঘ

  always h(x)
• The cost function for logistic regression and linear regression are the same.
  ক

  True

  খ

  False
• Threshold value is 0.6. h(x) = 0.3 for a particular instance. What is the value of y?
  ক

  0

  খ

  0.3

  গ

  0.7

  ঘ

  1
• Let g be the sigmoid function. Let a = -(infinite). What is the value of g(
  ক

  ? ] -1/2

  খ

  1

  গ

  1/2

  ঘ

  0
• The decision boundary is an important parameter in logistic regression.
  ক

  True

  খ

  False
• Let g be the sigmoid function. Let a = infinite. What is the value of g(
  ক

  ? ] 1/2

  খ

  -1

  গ

  1

  ঘ

  0
• Probability of an event occurring is 0.8. What is odds ratio?
  ক

  0.8:1

  খ

  4:1

  গ

  1:4

  ঘ

  2:0.8
• Probability of an event occurring is 0.2. What is odds ratio?
  ক

  -4:1

  খ

  4:1

  গ

  1:4

  ঘ

  1:0.4
• Let g be the sigmoid function. Let a >= 0. What is the value of g(
  ক

  ? ] g(] >= 1/2 <= 0 c) g(] <= 1/2 d) g(] >= 0 View Answer]

  খ

  g(a) <= 0

  গ

  g(a) <= 1/2

  ঘ

  g(a) >= 0
• Threshold value is 0.5. h(x) = 0.7 for a particular instance. What is the value of y?
  ক

  0

  খ

  0.3

  গ

  0.7

  ঘ

  1
• The value of a sigmoid function is the threshold value.
  ক

  True

  খ

  False
• h(x) > 0.6 -> y = 1. What does the value 0.6 represent?
  ক

  Cost function

  খ

  Threshold value

  গ

  Gradient descent

  ঘ

  Sigmoid function
• When was logistic regression invented?
  ক

  1968

  খ

  1958

  গ

  1948

  ঘ

  1988
• In a logistic regression problem an instance is similar to 60 positive instances, 20 negative instances, dissimilar to 30 positive instances, 90 negative instances. What kind of an instance is this?
  ক

  Negative instance

  খ

  Positive instance

  গ

  Cannot be determined, even if the threshold is given

  ঘ

  Can be determined, if the threshold is given
• An artificially intelligent car knows if to brake or not based on its distance from the car in front of it. Logistic regression algorithm is used.
  ক

  True

  খ

  False
• Who invented logistic regression?
  ক

  Vapnik

  খ

  Ross Quinlan

  গ

  DR Cox

  ঘ

  Chervonenkis
• The output in a logistic regression problem is yes (equivalent to 1 or true). What is its possible value?
  ক

  Greater than 0.5

  খ

  Depends on the algorithm’s threshold value

  গ

  Greater than 0.6

  ঘ

  Equal to 1
• In a logistic regression problem, what is a possible output for a new instance?
  ক

  0.85

  খ

  -0.19

  গ

  1.20

  ঘ

  89%