Machine Learning MCQs with answers Page - 15

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Q. In the regression equation Y = 75.65 + 0.50X, the intercept is

  • (A) 0.5
  • (B) 75.65
  • (C) 1
  • (D) indeterminable

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Q. Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.

  • (A) TRUE
  • (B) Classification
  • (C) ---
  • (D) ---

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Q. if there is only a discrete number of possible outcomes (called categories), the process becomes a . . . . . . . .

  • (A) Regression
  • (B) 1 is false, 2 is true
  • (C) Modelfree
  • (D) Categories

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Q. Lasso can be interpreted as least-squares linear regression where

  • (A) weights are regularized with the l1 norm
  • (B) the weights have a gaussian prior
  • (C) weights are regularized with the l2 norm
  • (D) the solution algorithm is simpler

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Q. Which of the following method is used to find the optimal features for cluster analysis

  • (A) k-means
  • (B) density-based spatial clustering
  • (C) spectral clustering find clusters
  • (D) all above

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Q. Perceptron Classifier is

  • (A) unsupervised learning algorithm
  • (B) semi-supervised learning algorithm
  • (C) supervised learning algorithm
  • (D) soft margin classifier

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Q. The correlation coefficient for two real-valued attributes is -0.85. What does this value tell you?

  • (A) the attributes are not linearly related.
  • (B) as the value of one attribute increases the value of the second attribute also increases
  • (C) as the value of one attribute decreases the value of the second attribute increases
  • (D) the attributes show a linear relationship

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Q. There are also many univariate methods that can be used in order to select the best features according to specific criteria based on . . . . . . . .

  • (A) F-tests and p-values
  • (B) chi-square
  • (C) ANOVA
  • (D) All above

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Q. Which of the following is an application of NN (Neural Network)?

  • (A) sales forecasting
  • (B) data validation
  • (C) risk management
  • (D) all of the mentioned

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Q. Which statement is true about the K-Means algorithm?

  • (A) the output attribute must be cateogrical
  • (B) all attribute values must be categorical
  • (C) all attributes must be numeric
  • (D) attribute values may be either categorical or numeric

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