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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 • 51.43K Points
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Q. 341) In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called         .

(A) deep learning
(B) machine learning
(C) reinforcement learning
(D) unsupervised learning
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Mr. Dubey • 51.43K Points
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Q. 342) 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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Mr. Dubey • 51.43K Points
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Q. 343)             showed better performance than other approaches, even without a context- based model

(A) machine learning
(B) deep learning
(C) reinforcement learning
(D) supervised learning
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Mr. Dubey • 51.43K Points
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Q. 344) Which of the following method is used to find the optimal features for cluster analysis

(A) k-means
(B) density-based spatial clustering
(C) spectral clustering find clusters
(D) all above
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Mr. Dubey • 51.43K Points
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Q. 345) scikit-learn also provides functions for creating dummy datasets from scratch:

(A) make_classification()
(B) make_regression()
(C) make_blobs()
(D) all above
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Mr. Dubey • 51.43K Points
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Q. 346)          which can accept a NumPy RandomState generator or an integer seed.

(A) make_blobs
(B) random_state
(C) test_size
(D) training_size
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Mr. Dubey • 51.43K Points
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Q. 347) In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikit-learn offers at least         valid options

(A) 1
(B) 2
(C) 3
(D) 4
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Mr. Dubey • 51.43K Points
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Q. 348) In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0.

(A) labelencoder class
(B) dictvectorizer
(C) labelbinarizer class
(D) featurehasher
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Mr. Dubey • 51.43K Points
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Q. 349)            is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky.

(A) removing the whole line
(B) creating sub-model to predict those features
(C) using an automatic strategy to input them according to the other known values
(D) all above
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Mr. Dubey • 51.43K Points
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Q. 350) It's possible to specify if the scaling process must include both mean and standard deviation using the parameters              .

(A) with_mean=true/false
(B) with_std=true/false
(C) both a & b
(D) none of the mentioned
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