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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 14

Q1 | Features being classified is                    of each other in Nave Bayes Classifier
  • independent
  • dependent
  • partial dependent
  • none
Q2 | Bayes Theorem is given by where 1. P(H) is the probability of hypothesis H being true.2. P(E) is the probability of the evidence(regardless of the hypothesis).3. P(E|H) is the probability of the evidence given that hypothesis is true.4. P(H|E) is the probability of the hypothesis given that the evidence is there.
  • true
  • false
Q3 | In given image, P(H|E) is                  probability.
  • posterior
  • prior
Q4 | In given image, P(H)is                  probability.
  • posterior
  • prior
Q5 | Conditional probability is a measure of the probability of an event given that another
  • true
  • false
Q6 | Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event.
  • true
  • false
Q7 | Bernoulli Nave Bayes Classifier is                    distribution
  • continuous
  • discrete
  • binary
Q8 | Multinomial Nave Bayes Classifier is                    distribution
  • continuous
  • discrete
  • binary
Q9 | Gaussian Nave Bayes Classifier is                    distribution
  • continuous
  • discrete
  • binary
Q10 | Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features.
  • true
  • false
Q11 | Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the               of the feature values.
  • mean
  • variance
  • discrete
  • random
Q12 | SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx)
  • true
  • false
Q13 | Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian.
  • true
  • false
Q14 | Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting
  • true
  • false
Q15 | SVM is a algorithm
  • classification
  • clustering
  • regression
  • all
Q16 | SVM is a learning
  • supervised
  • unsupervised
  • both
  • none
Q17 | The linearSVMclassifier works by drawing a straight line between two classes
  • true
  • false
Q18 | Which of the following function provides unsupervised prediction ?
  • cl_forecastb
  • cl_nowcastc
  • cl_precastd
  • none of the mentioned
Q19 | Which of the following is characteristic of best machine learning method ?
  • fast
  • accuracy
  • scalable
  • all above
Q20 | What are the different Algorithm techniques in Machine Learning?
  • supervised learning and semi-supervised learning
  • unsupervised learning and transduction
  • both a & b
  • none of the mentioned
Q21 | What is the standard approach to supervised learning?
  • split the set of example into the training set and the test
  • group the set of example into the training set and the test
  • a set of observed instances tries to induce a general rule
  • learns programs from data
Q22 | Which of the following is not Machine Learning?
  • artificial intelligence
  • rule based inference
  • both a & b
  • none of the mentioned
Q23 | What is Model Selection in Machine Learning?
  • the process of selecting models among different mathematical models, which are used to describe the same data set
  • when a statistical model describes random error or noise instead of underlying relationship
  • find interesting directions in data and find novel observations/ database cleaning
  • all above
Q24 | Which are two techniques of Machine Learning ?
  • genetic programming and inductive learning
  • speech recognition and regression
  • both a & b
  • none of the mentioned
Q25 | Even if there are no actual supervisors               learning is also based on feedback provided by the environment
  • supervised
  • reinforcement
  • unsupervised
  • none of the above