# Machine Learning: MCQs Set – 23

#### Q221: The k-means algorithm is a

• (A) Supervised learning algorithm
• (B) Unsupervised learning algorithm
• (C) Semi-supervised learning algorithm
• (D) Weakly supervised learning algorithm

#### Q222: When the number of features increase

• (A) Computation time increases
• (B) Model becomes complex
• (C) Learning accuracy decreases
• (D) All of the above

#### Q223: For unsupervised learning we have ____ model.

• (A) interactive
• (B) predictive
• (C) descriptive
• (D) prescriptive

#### Q224: Engineering a good feature space is a crucial ___ for the success of any machine learning model.

• (A) Pre-requisite
• (B) Process
• (C) Objective
• (D) None of the above

#### Q225: In LDA, intra-class and inter-class ___ matrices are calculated.

• (A) Scatter
• (C) Similarity
• (D) None of the above

#### Q226: We can define this probability as p(A|B) = p(A,B)/p(B) if p(B) > 0

• (A) Conditional probability
• (B) Marginal probability
• (C) Bayes probability
• (D) Normal probability

#### Q227: Predicting whether a tumour is malignant or benign is an example of?

• (A) Unsupervised Learning
• (B) Supervised Regression Problem
• (C) Supervised Classification Problem
• (D) Categorical Attribute

#### Q228: This refers to the transformations applied to the identified data before feeding the same into the algorithm.

• (A) Problem Identification
• (B) Identification of Required Data
• (C) Data Pre-processing
• (D) Definition of Training Data Set

#### Q229: Which of the following is true about SVM?

• (A) It is useful only in high-dimensional spaces
• (B) It requires less memory
• (C) SVM does not perform well when we have a large data set
• (D) SVM performs well when we have a large data set

#### Q230: When you find many noises in data, which of the following options would you consider in kNN?

• (A) Increase the value of k
• (B) Decrease the value of k
• (C) Noise does not depend on k
• (D) k = 0