ML unit-1 2-marks Level 3 n 4 PDF

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Machine Learning MCQS'S which are valued at 2 marks each on 1st Unit according to SPPU 2019 Pattern...

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Consider that you are analyzing a large collection of fraudulent credit card transactions to discover if there are sub-types of these transactions. Which of the following learning methods best describes the given learning problem? A) a) Reinforcement Learning B) b) Supervised Learning C) c) Unsupervised Learning D) d) Semi-supervised learning ANSWER: C A FEATURE F1 CAN TAKE CERTAIN VALUE: A, B, C, D, E, & F AND REPRESENTS GRADE OF STUDENTS FROM A COLLEGE.1) WHICH OF THE FOLLOWING STATEMENT IS TRUE IN FOLLOWING CASE? A) A) Feature F1 is an example of nominal variable. B) B) Feature F1 is an example of ordinal variable. C) C) It doesn’t belong to any of the above category. D) D) Both of these ANSWER: B The process of forming general concept definitions from examples of concepts to be learned. A) a) Deduction B) b) abduction C) c) induction D) d) conjunction ANSWER: C Imagine a newly-born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up. Specify what type of machine learning algorithm is best suited to do the same A) Supervised B) Unsupervised C) Reinforcement D) Semi-supervised ANSWER: C

Data used to build a data mining model A) a) validation data B) b) training data C) c) test data D) d) hidden data ANSWER: B Supervised learning and unsupervised clustering both require at least one A) a) hidden attribute. B) b) output attribute. C) c) input attribute. D) d) categorical attribute. ANSWER: A Supervised learning differs from unsupervised clustering in that supervised learning requires A) a) at least one input attribute. B) b) input attributes to be categorical. C) c) at least one output attribute. D) d) output attributes to be categorical ANSWER: B PCA is used for A) a. Dimensionality Enhancement B) b. Dimensionality Reduction C) c. Both D) d. None ANSWER: B PCA is used for A) a. Supervised Classification B) b. Unsupervised Classification

C) c. Semi-supervised Classification D) d. Cannot be used for classification ANSWER: B Automated vehicle is an example of ______ A) a) Supervised learning B) b) Unsupervised learning C) c) Active learning D) d) Reinforcement learning ANSWER: A In an Unsupervised learning ____________ A) a) Specific output values are given B) b) Specific output values are not given C) c) No specific Inputs are given D) d) Both inputs and outputs are given ANSWER: B Sentiment Analysis is an example of: a)Regression, b)Classification c)Clustering d)Reinforcement Learning A) A. 1 Only B) B. 1 and 2 C) C. 1 and 3 D) D. 1, 2 and 4 ANSWER: D What is Reinforcement learning? A) a) All data is unlabelled and the algorithms learn to inherent structure from the input data B) b) All data is labelled and the algorithms learn to predict the output from the input data C) c) It is a framework for learning where an agent interacts with an environment and receives a reward for each interaction D) d) Some data is labelled but most of it is unlabelled and a mixture of supervised and unsupervised techniques can be used.

ANSWER: C Simple regression assumes a __________ relationship between the input attribute and output attribute. A) A.Linear B) B.Quadratic C) C.reciprocal D) D.None of the above ANSWER: A In Supervised learning, class labels of the training samples are A) a. Known B) b. Unknown C) c. Doesn’t matter D) d. Partially known ANSWER: A Feature Selection means A) A. find a smaller subset of a many-dimensional data set to create a data model B) B. transforming high-dimensional data into spaces of fewer dimensions C) C.Both A & B D) D.None of the above ANSWER: A What happens when you get features in lower dimensions using PCA? 1.The features will still have interpretability 2.The features will lose interpretability 3.The features must carry all information present in data 4.The features may not carry all information present in data A) A. 1 and 3 B) B. 1 and 4 C) C. 2 and 3 D) D. 2 and 4 ANSWER: D

What would be the ideal complexity of the curve which can be used for separating the two [linear.png] A) A) Linear B) B) Quadratic C) C) Cubic D) D) insufficient data to draw conclusion ANSWER: A In an Unsupervised learning ____________ A) a) Specific output values are given B) b) Specific output values are not given C) c) No specific Inputs are given D) d) Both inputs and outputs are given ANSWER: B Sentiment Analysis is an example of: a)Regression, b)Classification c)Clustering d)Reinforcement Learning A) A. 1 Only B) B. 1 and 2 C) C. 1 and 3 D) D. 1, 2 and 4 ANSWER: D...