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This set of Machine Learning (ML) Multiple Choice Questions & Answers (MCQs) focuses on Machine Learning Set 25

Q1 | For a multiple regression model, SST = 200 and SSE = 50. The multiple coefficient ofdetermination is
  • 0.25
  • 4.00
  • 0.75
  • none of the above
Q2 | A nearest neighbor approach is best used
  • with large-sized datasets.
  • when irrelevant attributes have been removed from the data.
  • when a generalized model of the data is desirable.
  • when an explanation of what has been found is of primary importance.
Q3 | Another name for an output attribute.
  • predictive variable
  • independent variable
  • estimated variable
  • dependent variable
Q4 | Classification problems are distinguished from estimation problems in that
  • classification problems require the output attribute to be numeric.
  • classification problems require the output attribute to be categorical.
  • classification problems do not allow an output attribute.
  • classification problems are designed to predict future outcome.
Q5 | Which statement is true about prediction problems?
  • the output attribute must be categorical.
  • the output attribute must be numeric.
  • the resultant model is designed to determine future outcomes.
  • the resultant model is designed to classify current behavior.
Q6 | Which of the following is a common use of unsupervised clustering?
  • detect outliers
  • determine a best set of input attributes for supervised learning
  • evaluate the likely performance of a supervised learner model
  • determine if meaningful relationships can be found in a dataset
Q7 | The average positive difference between computed and desired outcome values.
  • root mean squared error
  • mean squared error
  • mean absolute error
  • mean positive error
Q8 | Selecting data so as to assure that each class is properly represented in both the training andtest set.
  • cross validation
  • stratification
  • verification
  • bootstrapping
Q9 | The standard error is defined as the square root of this computation.
  • the sample variance divided by the total number of sample instances.
  • the population variance divided by the total number of sample instances.
  • the sample variance divided by the sample mean.
  • the population variance divided by the sample mean.
Q10 | Data used to optimize the parameter settings of a supervised learner model.
  • training
  • test
  • verification
  • validation
Q11 | Bootstrapping allows us to
  • choose the same training instance several times.
  • choose the same test set instance several times.
  • build models with alternative subsets of the training data several times.
  • test a model with alternative subsets of the test data several times.
Q12 | The correlation coefficient for two real-valued attributes is –0.85. What does this value tell you?
  • the attributes are not linearly related.
  • as the value of one attribute increases the value of the second attribute also increases.
  • as the value of one attribute decreases the value of the second attribute increases.
  • the attributes show a curvilinear relationship.
Q13 | The average squared difference between classifier predicted output and actual output.
  • mean squared error
  • root mean squared error
  • mean absolute error
  • mean relative error
Q14 | Simple regression assumes a __________ relationship between the input attribute and outputattribute.
  • linear
  • quadratic
  • reciprocal
  • inverse
Q15 | Regression trees are often used to model _______ data.
  • linear
  • nonlinear
  • categorical
  • symmetrical
Q16 | The leaf nodes of a model tree are
  • averages of numeric output attribute values.
  • nonlinear regression equations.
  • linear regression equations.
  • sums of numeric output attribute values.
Q17 | Logistic regression is a ________ regression technique that is used to model data having a_____outcome.
  • linear, numeric
  • linear, binary
  • nonlinear, numeric
  • nonlinear, binary
Q18 | This technique associates a conditional probability value with each data instance.
  • linear regression
  • logistic regression
  • simple regression
  • multiple linear regression
Q19 | This supervised learning technique can process both numeric and categorical input attributes.
  • linear regression
  • bayes classifier
  • logistic regression
  • backpropagation learning
Q20 | With Bayes classifier, missing data items are
  • treated as equal compares.
  • treated as unequal compares.
  • replaced with a default value.
  • ignored.
Q21 | This clustering algorithm merges and splits nodes to help modify nonoptimal partitions.
  • agglomerative clustering
  • expectation maximization
  • conceptual clustering
  • k-means clustering
Q22 | This clustering algorithm initially assumes that each data instance represents a single cluster.
  • agglomerative clustering
  • conceptual clustering
  • k-means clustering
  • expectation maximization
Q23 | This unsupervised clustering algorithm terminates when mean values computed for the currentiteration of the algorithm are identical to the computed mean values for the previous iteration.
  • agglomerative clustering
  • conceptual clustering
  • k-means clustering
  • expectation maximization
Q24 | Machine learning techniques differ from statistical techniques in that machine learning methods
  • typically assume an underlying distribution for the data.
  • are better able to deal with missing and noisy data.
  • are not able to explain their behavior.
  • have trouble with large-sized datasets.
Q25 | In reinforcement learning if feedback is negative one it is defined as____.
  • Penalty
  • Overlearning
  • Reward
  • None of above