Classification Techniques Questions and Answers

Q: A statistical method used to make numerical predictions is ................. .

The method is Regression.Regression analysis models the relationship between a dependent variable and one or more independent variables, for example by fitting a line in linear regression, and uses it to predict a continuous numerical value, such as a house price from its size and location. It is the standard data mining method for numeric prediction.A support vector machine is mainly used for classification into categories. A hidden Markov model is a probabilistic model of sequences of hidden states, used in speech and sequence labelling. Link analysis studies the connections between nodes in a network, as in PageRank.

A statistical method used to make numerical predictions is ................. .

A.Regression
B.Support vector machine
C.Hidden Markov model
D.Link analysis

Exam Year(s)

MH SET - 2019, SET Exam - 2019,

Answer: A

The method is Regression.

Regression analysis models the relationship between a dependent variable and one or more independent variables, for example by fitting a line in linear regression, and uses it to predict a continuous numerical value, such as a house price from its size and location. It is the standard data mining method for numeric prediction.

A support vector machine is mainly used for classification into categories. A hidden Markov model is a probabilistic model of sequences of hidden states, used in speech and sequence labelling. Link analysis studies the connections between nodes in a network, as in PageRank.

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The information gain, one of the attribute selection measures, used in partition of data (D), is expressed as

Info (D) = .................

where \(p_i\) is the probability that an arbitrary tuple in D belongs to class \(c_i\) :

A.\(-\sum_{i=1}^{m} p_i \log(p_i)\)
B.\(\sum_{i=1}^{m} p_i \log(p_i)\)
C.\(-\sum_{i=1}^{m} p_i \log_2(p_i)\)
D.\(\sum_{i=1}^{m} p_i \log_2(p_i)\)

Exam Year(s)

MH SET - 2020, SET Exam - 2020,

Answer: C

The expected information is \(\text{Info}(D) = -\sum_{i=1}^{m} p_i \log_2(p_i)\).

Info(D), the entropy of D, is the average number of bits needed to identify the class of a tuple. Each class \(c_i\) with probability \(p_i\) contributes \(-p_i \log_2 p_i\); the logarithm is to base 2 because information is measured in bits, and the minus sign makes the result non-negative, since \(\log_2 p_i \le 0\) for \(0 \lt p_i \le 1\).

The options without the minus sign would give a negative value, and those with an unspecified base do not give the measure in bits used in ID3's information gain.

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Classification Techniques Questions and Answers

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