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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