Naive-Bayes Algorithm MCQ

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• “It is easy and fast to predict the class of the test data set by using Naïve Bayes algorithm”. Which of the following statement contradicts the above given statement?
  ক

  Because there is no iteration

  খ

  Because there is no epoch

  গ

  Because there is an error back propagation

  ঘ

  Because there are no operations involved in solving a matrix problem
• Arrange the following steps in sequence in order to calculate the probability of an event through Naïve Bayes classifier. I. Find the likelihood probability with each attribute for each class. II. Calculate the prior probability for given class labels. III. Put these values in Bayes formula and calculate posterior probability. IV. See which class has a higher probability, given the input belongs to the higher probability class.
  ক

  I → II → III → IV

  খ

  II → I → III → IV

  গ

  III → II → I → IV

  ঘ

  II → III → I → IV
• Which one of the following applications is not an example of Naïve Bayes algorithm?
  ক

  Spam filtering

  খ

  Text classification

  গ

  Stock market forecasting

  ঘ

  Sentiment analysis
• Identify the parametric machine learning algorithm.
  ক

  CNN (Convolutional neural network)

  খ

  KNN (K-Nearest Neighbours)

  গ

  Naïve Bayes

  ঘ

  SVM (Support vector machines)
• In which of the following case the Naïve Bayes’ algorithm does not work well?
  ক

  When faster prediction is required

  খ

  When the Naïve assumption holds true

  গ

  When there is the case of Zero Frequency

  ঘ

  When there is a multiclass prediction
• Is the assumption of the Naïve Bayes algorithm a limitation to use it?
  ক

  True

  খ

  False
• Which one of the following terms is not used in the Bayes’ Theorem?
  ক

  Prior

  খ

  Unlikelihood

  গ

  Posterior

  ঘ

  Evidence
• What is the assumptions of Naïve Bayesian classifier?
  ক

  It assumes that features of a data are completely dependent on each other

  খ

  It assumes that each input variable is dependent and the model is not generative

  গ

  It assumes that each input attributes are independent of each other and the model is generative

  ঘ

  It assumes that the data dimensions are dependent and the model is generative
• Which of the following statement is not true about Naïve Bayes classifier algorithm?
  ক

  It cannot be used for Binary as well as multi-class classifications

  খ

  It is the most popular choice for text classification problems

  গ

  It performs well in Multi-class prediction as compared to other algorithms

  ঘ

  It is one of the fast and easy machine learning algorithms to predict a class of test datasets
• Naïve Bayes classifier algorithms are mainly used in text classification.
  ক

  True

  খ

  False