Machine Learning MCQ Questions And Answers

The following quiz “Machine Learning MCQ Questions And Answers” provides Multiple Choice Questions (MCQs) related to Machine Learning. These machine learning MCQs are also Interviews (campus interview, walk-in interview, company interview), Placement or recruitment, entrance examinations, and competitive examinations oriented. You can practice these below questions to improve your Machine Learning skills. You can click on the View Answer button to check the answer if you needed. Let’s solve this Machine Learning MCQ Questions And Answers quiz.

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01. What is Machine learning?

  1. The autonomous acquisition of knowledge through the use of computer programs
  2. The autonomous acquisition of knowledge through the use of manual programs
  3. The selective acquisition of knowledge through the use of computer programs
  4. The selective acquisition of knowledge through the use of manual programs

Answer : A
Explanation: “Machine learning” is the autonomous acquisition of knowledge through the use of computer programs.

02. What is true about Machine Learning?

  1. Machine Learning (ML) is the field of computer science
  2. ML is a type of artificial intelligence that extract patterns out of raw data by using an algorithm or method
  3. The main focus of ML is to allow computer systems learn from experience without being explicitly programmed or human intervention
  4. All of the above

Answer : D
Explanation: All the statements are true about Machine Learning.

03. ML is a field of AI consisting of learning algorithms that?

  1. Improve their performance
  2. At executing some task
  3. Over time with experience
  4. All of the above

Answer : D
Explanation: Machine learning is a field of AI consisting of learning algorithms that: Improve their performance (P), At executing some task (T), Over time with experience (E).

04. Different learning methods do not include?

  1. Memorization
  2. Analogy
  3. Introduction
  4. Deduction

Answer : C
Explanation: Different learning methods in the ML do not include Introdution.

05. Which of the following is a widely used and effective machine learning algorithm based on the idea of bagging?

  1. Decision Tree
  2. Random Forest
  3. Regression
  4. Classification

Answer : B
Explanation: Random Forest

06. High entropy means that the partitions in classification are

  1. pure
  2. not pure
  3. useful
  4. useless

Answer : B
Explanation: Entropy is a measure of the randomness in the information being processed So the higher the entropy, the harder it is to draw any conclusions from that information. Entropy is a measure of disorder or purity or unpredictability or uncertainty. So Low entropy means less uncertain and high entropy means more uncertain.

07. Which of the following are ML methods?

  1. Based on human supervision
  2. Supervised Learning
  3. Semi-reinforcement Learning
  4. All of the above

Answer : A
Explanation: The following are various Machine learning methods based on some broad categories: Based on human supervision, Unsupervised Learning, Semi-supervised Learning, and Reinforcement Learning.

08. In language understanding, the levels of knowledge do not include?

  1. Phonological
  2. Syntactic
  3. Empirical
  4. Logical

Answer : C
Explanation: In language understanding, the levels of knowledge do not include empirical knowledge.

09. A machine learning problem involves four attributes plus a class. The attributes have 3, 2, 2, and 2 possible values each. The class has 3 possible values. How many maximum possible different examples are there?

  1. 12
  2. 24
  3. 48
  4. 72

Answer : D
Explanation: Maximum possible different examples are the products of the possible values of each attribute and the number of classes so the result would be
3 * 2 * 2 * 2 * 3 = 72

10. When performing regression or classification, which of the following is the correct way to preprocess the data?

  1. Normalize the data → PCA → training
  2. PCA → normalize PCA output → training
  3. Normalize the data → PCA → normalize PCA output → training
  4. None of the above

Answer : A
Explanation: First Normalize the data then PCA then training.

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