Machine Learning Multiple Choice Questions with Answers 29

Top 3 Machine Learning Quiz Questions with Answers explanation, Interview questions on machine learning, quiz questions for data scientist answers explained, machine learning exam questions, question bank in machine learning, lazy learner, k-nearest neighbor, eager learner, SVM classifier

 

Machine learning Quiz Questions - Set 29

 

1. Which of the following is a lazy learner?

a) k-nearest neighbor

b) Linear regression

c) Support Vector Machine

d) Artificial Neural Network

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Ans : (a)

Answer: (a) k-nearest neighbor k-NN is a lazy learner because it doesn’t learn a discriminative function from the training data but “memorizes” the training dataset instead. There is no training time in k-NN. Due to this, the prediction step is expensive. k-NN does not generalize the input training data. What is lazy learner? In machine learning, lazy learning is a learning method in which generalization of the training data is, in theory, delayed until a query is made to the system, as opposed to eager learning, where the system tries to generalize the training data before receiving queries.

 

2. Which of the following is a disadvantage of k-Nearest Neighbors algorithm?

a) Low accuracy

b) Insensitive to outliers

c) Computationally expensive

d) Need very less memory

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Ans : (c)

Answer: (c) Computationally expensive Due to the absence of training time, k-NN has to involve the entire dataset during the prediction step. This particular property makes k-NN a computationally expensive classification algorithm.

 

3. Which of the following is an eager learner?

a) k-nearest neighbor

b) Support Vector Machine

c) case-based reasoning

d) None of the above

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Ans : (b)

Answer: (b) Support Vector Machine SVM is a classification method for classifying both linear and non-linear data. SVM builds a hyperplane classification model during training. It finds this hyperplane using support vectors (“essential” training tuples) and margins (defined by the support vectors). What is eager learner? Eager learning is a learning method in which the system tries to construct a general, input-independent target function during training of the system. Eager learners, when given a set of training tuples, will  construct  a  generalization  (i.e.,  classification)  model before  receiving new  e.g., test)  tuples  to  classify.

 

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