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Question 1

Classifiers, such as naive Bayes, can be used to: (check all that apply)

decide whether two documents are exact duplicates

recognize common objects, such as faces, animals, etc.

make predictions even in the absence of past data

detect potentially fraudulent behavior

Question 2

The mutual information between two signals, or sets of data is a measure of

how rare the signals or data values are

how often the value of one signal determines the value of the other

the probability that both the signals take equal values

whether the signals are random

Question 3

Advertisers would ideally like to

spend more money on an advertisement that is seen by a large number of users vs one that is seen by exactly those who most likely to buy their product.

act on signals that have high mutual information with what consumers are looking for

minimize the mutual information in order to lower costs and confuse competitors

measure the mutual information between consumers so they can determine which ones are friends of each other

Question 4

What is TF-IDF (i.e. term-frequency x document-frequency)?

One way to determine which keywords best represent a document

A measure of how rare a word is

A way to determine which words are the most frequently used ones

A measure of how close two words are in meaning

Question 5

Which of the following assumptions are often incorrect, and require care while constructing classifiers for prediction:

Using more features will always improve classification accuracy

Using features with a high mutual information with respect to the class being predicted is desirable

Words occurring in a document do not depend on which other words occur

Features are often dependent

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Web Intelligence and Big Data

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