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Dear candidates you will find MCQ questions of Machine Learning (ML) here. Learn these questions and prepare yourself for coming examinations and interviews. You can check the right answer of any question by clicking on any option or by clicking view answer button.

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Mr. Dubey • 51.17K Points
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Q. 221) The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?

(A) the attributes are not linearly related.
(B) as the value of one attribute increases the value of the second attribute also increases
(C) as the value of one attribute decreases the value of the second attribute increases
(D) the attributes show a linear relationship
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Mr. Dubey • 51.17K Points
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Q. 222) 8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters,
C1, C2, C3 has following observations:
C1: {(2,2), (4,4), (6,6)}
C2: {(0,4), (4,0),(2,5)}
C3: {(5,5), (9,9)}
What will be the cluster centroids if you want to proceed for second iteration?

(A) c1: (4,4), c2: (2,2), c3: (7,7)
(B) c1: (6,6), c2: (4,4), c3: (9,9)
(C) c1: (2,2), c2: (0,0), c3: (5,5)
(D) c1: (4,4), c2: (3,3), c3: (7,7)
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Mr. Dubey • 51.17K Points
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Q. 223) In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"?

(A) p(x/c)
(B) p(c/x)
(C) p(c)
(D) p(x)
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Mr. Dubey • 51.17K Points
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Q. 224) Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance
2. Generalized models
3. Better interpretability

(A) 1 and 3
(B) 2 and 3
(C) 1, 2 and 3
(D) 1 and 2
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Mr. Dubey • 51.17K Points
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Q. 225) What is back propagation?

(A) it is another name given to the curvy function in the perceptron
(B) it is the transmission of error back through the network to adjust the inputs
(C) it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn
(D) none of the mentioned
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Mr. Dubey • 51.17K Points
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Q. 226) Which of the following is an application of NN (Neural Network)?

(A) sales forecasting
(B) data validation
(C) risk management
(D) all of the mentioned
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Mr. Dubey • 51.17K Points
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Q. 227) Neural Networks are complex ______________ with many parameters.

(A) linear functions
(B) nonlinear functions
(C) discrete functions
(D) exponential functions
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Mr. Dubey • 51.17K Points
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Q. 228) Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results.

(A) true – this works always, and these multiple perceptrons learn to classify even complex problems
(B) false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do
(C) true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded
(D) false – just having a single perceptron is enough
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Q. 229) Which one of the following is not a major strength of the neural network approach?

(A) neural network learning algorithms are guaranteed to converge to an optimal solution
(B) neural networks work well with datasets containing noisy data
(C) neural networks can be used for both supervised learning and unsupervised clustering
(D) neural networks can be used for applications that require a time element to be included in the data
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Q. 230) The network that involves backward links from output to the input and hidden layers is called

(A) self organizing maps
(B) perceptrons
(C) recurrent neural network
(D) multi layered perceptron
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