Machine Learning MCQs with answers Page - 7

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📊 Machine Learning
Q. 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?
  • (A) 12
  • (B) 24
  • (C) 48
  • (D) 72
💬 Discuss
✅ Correct Answer: (D) 72

Explanation: 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?

📊 Machine Learning
Q. In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea
  • (A) TRUE
  • (B) accuracy
  • (C) ---
  • (D) ---
💬 Discuss
✅ Correct Answer: (A) TRUE

Explanation: In machine learning, instability refers to the sensitivity of an algorithm to changes in the training data. When an algorithm is unstable, small variations in the training data can lead to significant changes in the learned classifiers. Bagging, which stands for Bootstrap Aggregating, is a technique that aims to reduce the variance and improve the stability of machine learning models.

📊 Machine Learning
Q. Which of the following is characteristic of best machine learning method ?
  • (A) fast
  • (B) are better able to deal with missing and noisy data.
  • (C) scalable
  • (D) all above
💬 Discuss
✅ Correct Answer: (D) all above

Explanation: Machine learning methods can vary widely in terms of their characteristics and suitability for different tasks. The "best" machine learning method depends on the specific requirements and goals of the problem at hand. Let's evaluate each option:

📊 Machine Learning
Q. Machine learning techniques differ from statistical techniques in that machine learning methods
  • (A) typically assume an underlying distribution for the data.
  • (B) when a statistical model describes random error or noise instead of underlying relationship
  • (C) are not able to explain their behavior.
  • (D) have trouble with large-sized datasets.
💬 Discuss
✅ Correct Answer: (A) typically assume an underlying distribution for the data.

Explanation: Machine learning techniques and statistical techniques are related fields, but they have distinct differences in their approaches and characteristics.

📊 Machine Learning
Q. What is Model Selection in Machine Learning?
  • (A) The process of selecting models among different mathematical models, which are used to describe the same data set
  • (B) Interference
  • (C) Find interesting directions in data and find novel observations/ database cleaning
  • (D) All above
💬 Discuss
✅ Correct Answer: (A) The process of selecting models among different mathematical models, which are used to describe the same data set

Explanation: Model selection in machine learning refers to the process of choosing the most appropriate model or algorithm from a set of candidate models to make predictions or capture relationships within a given dataset.

📊 Machine Learning
Q. In simple term, machine learning is
  • (A) training based on historical data
  • (B) prediction to answer a query
  • (C) both A and B
  • (D) automization of complex tasks
💬 Discuss
✅ Correct Answer: (C) both A and B

Explanation: Machine learning, in simple terms, can be described as follows:

📊 Machine Learning
Q. Which of the following is the best machine learning method?
  • (A) scalable
  • (B) accuracy
  • (C) fast
  • (D) all of the above
💬 Discuss
✅ Correct Answer: (D) all of the above

Explanation: Which of the following is the best machine learning method?

📊 Machine Learning
Q. The output of training process in machine learning is
  • (A) machine learning model
  • (B) machine learning algorithm
  • (C) null
  • (D) accuracy
💬 Discuss
✅ Correct Answer: (A) machine learning model

Explanation: The output of the training process in machine learning is:

📊 Machine Learning
Q. Application of machine learning methods to large databases is called
  • (A) data mining.
  • (B) artificial intelligence
  • (C) big data computing
  • (D) internet of things
💬 Discuss
✅ Correct Answer: (A) data mining.

Explanation: Application of machine learning methods to large databases is called:

📊 Machine Learning
Q. If machine learning model output involves target variable then that model is called as
  • (A) descriptive model
  • (B) predictive model
  • (C) reinforcement learning
  • (D) all of the above
💬 Discuss
✅ Correct Answer: (B) predictive model

Explanation: If a machine learning model's output involves the target variable, then that model is called:

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