Cost Function MCQ

Test your knowledge with important Cost Function MCQ and their applications. These MCQs are beneficial for competitive exams too. Explore 30 + more Cost Function MCQs at Bissoy

• Very mall learning rate improves cost function.
  ক

  True

  খ

  False
• A variational problem is one in which we are looking for a function which can optimize a certain cost function.
  ক

  True

  খ

  False
• Which of the following statements is not true about cost function?
  ক

  Cost function is also called a loss or error function

  খ

  Its goal is to maximize the cost function

  গ

  We want to define a cost function to find the weight in the neural network

  ঘ

  It wants different cost functions for regression and classification problems
• When the cost function increases, the learning rate also increases.
  ক

  True

  খ

  False
• Which of the following is not a cost function requirement?
  ক

  The cost function must be able to be written as an average

  খ

  The cost function must not be dependent on any activation values of a neural network

  গ

  Technically a cost function can be dependent on any output values

  ঘ

  If the cost function is dependent on other activation layers besides the output one, back propagation will be valid
• Which of the following statements is not true about the cost function?
  ক

  It is a measure of how good a neural network did with respect to its given training sample and the expected output

  খ

  It depend on variables such as weights

  গ

  It is a single value, not a vector

  ঘ

  It never depends on bias
• Hypothesis is: h(x) = t0 + t1x. How do we graphically find the desired cost function?
  ক

  Plot J(t0, t1) against t0 and find minima

  খ

  Plot J(t0, t1)) against t1 and find minima

  গ

  Plot J(t0, t1) against either t1 or t0 and find minima

  ঘ

  Make a 3-d plot with J(t0, t1) against t1 and t0 and find minima
• What is the ideal value of t1?
  ক

  0

  খ

  Depends on the dataset

  গ

  1

  ঘ

  0.5
• How to graphically find t1 for which cost function is minimized?
  ক

  Plot J(t1) against t1 and find minima

  খ

  Plot t1 against J(t1) and find minima

  গ

  Plot J(t1) against t1 and find maxima

  ঘ

  Plot t1 against J(t1) and find maxima
• (x(1), y(1)) = 1, 1.5, (x(2), y(2)) = 2, 3, (x(3), y(3)) = 3, 4.5. Hypothesis: h(x) = t1x, where t1 = 2. How much error is obtained?
  ক

  0.3

  খ

  0

  গ

  0.42

  ঘ

  0.5
• (x(1), y(1)) = 1, 1.5, (x(2), y(2)) = 2, 3, (x(3), y(3)) = 3, 4.5. Hypothesis: h(x) = t1x, where t1 = 1.5. How much error is obtained?
  ক

  4.5

  খ

  0

  গ

  22.5

  ঘ

  1.5
• In the simplified hypothesis, what does hypothesis H and cost function J depend on?
  ক

  Both are functions of x

  খ

  J is a function of x, H is a function of t1

  গ

  H is a function of x, J is a function of t1

  ঘ

  Both are functions of t1
• The simplified hypothesis reduces the complexity of the cost function.
  ক

  True

  খ

  False
• What is the simplified hypothesis?
  ক

  h(x) = t1x

  খ

  h(x) = t0 + t1x

  গ

  h(x) = t0

  ঘ

  h(x) = t0x
• The cost function contains a summation expression.
  ক

  True

  খ

  False
• In a linear regression problem, h(x) is the predicted value of the target variable, y is the actual value of the target variable, m is the number of training examples. What do we try to minimize?
  ক

  (h(x) – y) / m

  খ

  (h(x) – y)2 / 2*m

  গ

  (h(x) – y) / 2*m

  ঘ

  (y – h(x))
• The hypothesis is given by h(x) = t0 + t1x. What does t1 = 0 after several iterations imply?
  ক

  The target variable is independent of x

  খ

  Hypothesis is wrong

  গ

  t0 is 0

  ঘ

  x is the target variable
• The hypothesis is given by h(x) = t0 + t1x. What is the goal of t0 and t1?
  ক

  Give negative h(x)

  খ

  Give h(x) as close to 0 as possible, without themselves being 0

  গ

  Give h(x) as close to y, in training data, as possible

  ঘ

  Give h(x) closer to x than y
• The hypothesis is given by h(x) = t0 + t1x. t0 gives the value of h(x) when x is 0.
  ক

  True

  খ

  False
• The hypothesis is given by h(x) = t0 + t1x. What are t0 and t1?
  ক

  Value of h(x) when x is 0, intercept along y-axis

  খ

  Value of h(x) when x is 0, the rate at which h(x) changes with respect to x

  গ

  The rate at which h(x) changes with respect to x, intercept along the y-axis

  ঘ

  Intercept along the y-axis, the rate at which h(x) changes with respect to x