Machine Learning MCQs with answers Page - 18

Here, you will find a collection of MCQ questions on Machine Learning. Go through these questions to enhance your preparation for upcoming examinations and interviews.

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Q. Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decision boundary will change?

  • (A) Yes
  • (B) determine a best set of input attributes for supervised learning
  • (C) ---
  • (D) ---

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Q. PCA can be used for projecting and visualizing data in lower dimensions.

  • (A) TRUE
  • (B) 1 is false, 2 is true
  • (C) ---
  • (D) ---

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Q. Which of the following is a common use of unsupervised clustering?

  • (A) detect outliers
  • (B) using information
  • (C) evaluate the likely performance of a supervised learner model
  • (D) determine if meaningful relationships can be found in a dataset

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Q. Which of the following is true about SVM?
1. Kernel function map low dimensional data to high dimensional space.
2. It is a similarity Function

  • (A) 1 is true, 2 is false
  • (B) group the set of example into the training set and the test
  • (C) 1 is true, 2 is true
  • (D) 1 is false, 2 is false

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Q. Multivariate split is where the partitioning of tuples is based on a combination of attributes rather than on a single attribute.

  • (A) TRUE
  • (B) they have high variance
  • (C) ---
  • (D) ---

A

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Q. MLE estimates are often undesirable because

  • (A) they are biased
  • (B) 0.6
  • (C) they are not consistent estimators
  • (D) none of the above

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Q. A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule "if milk and bread are purchased, then cheese is also purchased".

  • (A) 0.4
  • (B) it is a measure of purity
  • (C) 0.8
  • (D) 0.42

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Q. True or False: In boosting, individual base learners can be parallel.

  • (A) TRUE
  • (B) Autonomous car driving, Logistic optimization
  • (C) ---
  • (D) ---

A

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Q. What is gini index?

  • (A) it is a type of index structure
  • (B) ridge has smaller bias, larger variance
  • (C) both a and b
  • (D) none of the options

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Q. Common deep learning applications include . . . . . . . .

  • (A) Image classification, Real-time visual tracking
  • (B) 0.09
  • (C) Bioinformatics, Speech recognition
  • (D) All above

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