Machine Learning Set 25 MCQs

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