Machine Learning MCQ Questions with answers and solutions Page 17

Q. How can you avoid overfitting ?

(A) by using a lot of data
(B) by using inductive machine learning
(C) by using validation only
(D) none of above

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✅ Correct Answer: (A) by using a lot of data

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. What are the steps for using a gradient descent algorithm?
1. Calculate error between the actual value and the predicted value
2. Reiterate until you find the best weights of network
3. Pass an input through the network and get values from output layer
4. Initialize random weight and bias
5. Go to each neurons which contributes to the error and change its respective values to reduce the error

(A) 1, 2, 3, 4, 5
(B) 4, 3, 1, 5, 2
(C) 3, 2, 1, 5, 4
(D) 5, 4, 3, 2, 1

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✅ Correct Answer: (B) 4, 3, 1, 5, 2

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Problem in multi regression is ?

(A) multicollinearity
(B) overfitting
(C) both multicollinearity & overfitting
(D) underfitting

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✅ Correct Answer: (C) both multicollinearity & overfitting

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Tree/Rule based classification algorithms generate ... rule to perform the classification.

(A) if-then.
(B) while.
(C) do while
(D) switch.

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✅ Correct Answer: (A) if-then.

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. In the regression equation Y = 75.65 + 0.50X, the intercept is

(A) 0.5
(B) 75.65
(C) 1
(D) indeterminable

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✅ Correct Answer: (B) 75.65

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called . . . . . . . .

(A) Regression
(B) Accuracy
(C) Modelfree
(D) Scalable

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✅ Correct Answer: (C) Modelfree

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. We can also compute the coefficient of linear regression with the help of an analytical method called "Normal Equation". Which of the following is/are true about "Normal Equation"?
1. We don't have to choose the learning rate
2. It becomes slow when number of features is very large
3. No need to iterate

(A) 1 and 2
(B) 1 and 3
(C) 2 and 3
(D) 1, 2 and 3

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✅ Correct Answer: (D) 1, 2 and 3

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

(A) TRUE
(B) Classification
(C) ---
(D) ---

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✅ Correct Answer: (A) TRUE

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. if there is only a discrete number of possible outcomes (called categories), the process becomes a . . . . . . . .

(A) Regression
(B) 1 is false, 2 is true
(C) Modelfree
(D) Categories

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✅ Correct Answer: (B) 1 is false, 2 is true

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Q. Lasso can be interpreted as least-squares linear regression where

(A) weights are regularized with the l1 norm
(B) the weights have a gaussian prior
(C) weights are regularized with the l2 norm
(D) the solution algorithm is simpler

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✅ Correct Answer: (A) weights are regularized with the l1 norm

Published by Mr. Dubey ⭐ 111.54K Points at 26-Apr-2024 12:22 pm in Machine Learning

Machine Learning MCQs with answers Page - 17

Q. How can you avoid overfitting ?

Q. Problem in multi regression is ?

Q. Tree/Rule based classification algorithms generate ... rule to perform the classification.

Q. In the regression equation Y = 75.65 + 0.50X, the intercept is

Q. there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called . . . . . . . .

Q. Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

Q. if there is only a discrete number of possible outcomes (called categories), the process becomes a . . . . . . . .

Q. Lasso can be interpreted as least-squares linear regression where

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