Machine Learning (ISZC464)
Comprehensive Question Paper - 2016-2017 Regular
BITS PILANI WILP
Information Retrieval - SSZG537 - Comprehensive Question Paper - 2016-2017 Regular BITS PILANI WILP
Information Retrieval - SSZG537 -
Comprehensive Question Paper - 2016-2017 Regular BITS PILANI WILP
Information Retrieval - Quiz 2 - BITS PILANI WILP
Information Retrieval - SSZG537 - Quiz 2
BITS PILANI WILP
1. Which is true about clustering algorithms?
Select one:
a. Flat algorithms are those which create unrelated partitioning of documents into clusters.
b. Soft clustering algorithms are those in which a document belongs to exactly one cluster.
c. All of the above.
d. Hard clustering algorithms are those in which a document can belong to more than one cluster.
Ans: a. Flat algorithms are those which create unrelated partitioning of documents into clusters.
2. Rule-based machine translation in Cross-language Information retrieval involves:
Select one:
a. Involves very less semantic analysis.
b. Inter-lingua representation.
c. All of the above.
d. Involves very less syntactic analysis
Ans: c. All of the above.
3. The termination criteria for k-means algorithm is:
Select one:
a. Centroid positions don’t change
b. Terminate when the Residual Sum of Squares distance falls below a threshold.
c. All of the above.
d. A fixed number of iterations
Ans: c. All of the above.
4. Which is true about the Bernoulli model of text classification?
Select one:
a. It does not consider the probability of non-occurrence of the terms of the vocabulary in the test document.
b. It estimates P(t|c) as the fraction of documents of class c that contain term t
c. It considers the number of occurrences of the term in the test document.
d. It estimates P(t|c) as the fraction of tokens or fraction of positions in documents of class c that contain term t
Ans: d. It estimates P(t|c) as the fraction of tokens or fraction of positions in documents of class c that contain term t
5. Group-average agglomerative clustering (GAAC) is determined by:
Select one:
a. Average similarity of all document pairs including those from the same cluster but self-similarities are not included in the average.
b. Average similarity of all document pairs including those from the same cluster.
c. Average similarity of all document pairs excluding those from the same cluster.
d. Average similarity of all document pairs excluding those from the same cluster but self-similarities are not included in the average.
Ans: a. Average similarity of all document pairs including those from the same cluster but self-similarities are not included in the average.
Ref: http://nlp.stanford.edu/IR-book/html/htmledition/group-average-agglomerative-clustering-1.html
6. Document frequency of a term is the:
Select one:
a. Number of documents that contain the term.
b. None of the above.
c. Number of times the term appears in the document
d. Number of times the term appears in the collection.
Ans: a. Number of documents that contain the term.
7. Which is true about the IBM models?
Select one:
a. IBM model 5 adds a fertility factor.
b. IBM model 2 is an absolute reordering model whereas IBM model 4 is a relative reordering model.
c. IBM model 3 keeps track of available positions for output words
d. IBM model 4 is an absolute reordering model whereas IBM model 2 is a relative reordering model.
Ans: b. IBM model 2 is an absolute reordering model whereas IBM model 4 is a relative reordering model.
8. The idf-weight of a rare term is:
Select one:
a. Lower than frequent term.
b. No relation.
c. Higher than frequent term.
d. Same as frequent term.
Ans: c. Higher than frequent term.
9. Optimal clustering in k-means depends upon:
Select one:
a. None of the above.
b. No. of iterations
c. Seed choice
d. Choice of objective function
Ans: c. Seed choice
10. The criteria to determine the cuts in the dendrogram is:
Select one:
a. Cut the dendrogram where the gap between two successive combination similarities is largest.
b. Cut at a pre-specified level of similarity.
c. All of the above.
d. Cut the dendrogram to obtain a pre-specified number of clusters.
Ans: c. All of the above.
11. The most common hierarchical clustering algorithms have a complexity that is:
Select one:
a. At least linear in the number of documents
b. At most linear in the number of documents
c. At most quadratic in the number of documents
d. At least quadratic in the number of documents
Ans: d. At least quadratic in the number of documents
12. Boolean queries often result in:
Select one:
a. Too many or too few results
b. None of the above.
c. Too few results
d. Too many results.
Ans: a. Too many or too few results
13. Purity of clustering is 1 when:
Select one:
a. None of the above.
b. Each document gets its own cluster.
c. Each document gets atleast one cluster.
d. The number of clusters is large.
Ans: b. Each document gets its own cluster.
14. The decision boundary between 2 clusters in Rocchio classification is found by:
Select one:
a. Line at which all points are equidistant from the centroids of the 2 clusters.
b. Line at which atleast one point is equidistant from the centroids of the 2 clusters.
c. Line at which atmost 1 point is equidistant from the centroids of the 2 clusters.
d. None of the above.
Ans: a. Line at which all points are equidistant from the centroids of the 2 clusters.
15. The more frequent the query term in the document is:
Select one:
a. The lesser the score of the document.
b. Does not make any affect.
c. The higher the score of the document.
d. None of the above.
Ans: c. The higher the score of the document.
16. The objective or the partitioning criterion in k-means text clustering algorithm is to:
Select one:
a. Minimize the average squared difference from the centroid
b. Maximize the average squared difference from the centroid
c. Maximize the residual sum of squares distance for all the clusters.
d. Minimize the residual sum of squares distance for all the clusters.
Ans: a. Minimize the average squared difference from the centroid
17. Issues with the Jaccard coefficient are:
Select one:
a. It doesn’t consider term frequency.
b. It does not consider the fact that rare terms in a collection are more informative than frequent terms.
c. It is biased towards shorter documents.
d. All of the above.
Ans: d. All of the above.
18. The tf-idf weight of a term increases with:
Select one:
a. The length of the document.
b. The rarity of the term in the collection
c. The number of occurrences within a document
d. Both number of occurrences and rarity of the term.
Ans: d. Both number of occurrences and rarity of the term.
19. The best measure that is used to rank the documents is:
Select one:
a. Jaccard coefficient
b. Cosine similarity
c. Euclidean distance
d. N-gram overlap
Ans: b. Cosine similarity
20. Benefits of doing text clustering are:
Select one:
a. To improve retrieval recall
b. All of the above.
c. To compute better similarity scores.
d. To improve retrieval speed
Ans: b. All of the above.
21. kNN classification rule for k > 1 is:
Select one:
a. Assign each test document to the class of its nearest neighbour in the training set.
b. Assign each test document to the minority class of its k nearest neighbours in the training set.
c. Assign each test document to the majority class of its k nearest neighbours in the training set.
d. Assign each test document to a random class of its k nearest neighbours in the training set.
Ans: c. Assign each test document to the majority class of its k nearest neighbours in the training set.
22. Ranked retrieval models take as input:
Select one:
a. None of the above
b. Boolean queries
c. Logical queries
d. Free text queries
Ans: d. Free text queries
23. What is contiguity hypothesis in vector space classification?
Select one:
a. Documents from different classes don’t overlap
b. Documents in the same class form a contiguous region of space.
c. All of the above.
d. Intra-cluster similarity is higher than inter-cluster similarity
Ans: c. All of the above.
24. A document with 10 occurrences of the term is more relevant than a document with 1 occurrence of the term. What is the degree of this relevance?
Select one:
a. Same relevance.
b. 10 Times more relevant.
c. None of the above.
d. Log of term frequency.
Ans: d. Log of term frequency.
25. Which one is true about the Bag of words model?
Select one:
a. It considers a document as a collection of term frequencies.
b. It considers a document as a collection of terms
c. Vector representation doesn’t consider the ordering of words in a document.
d. All of the above.
Ans: d. All of the above.
Usability Engineering- SZG547 Mid-Semester Question Paper - 2016-2017 Regular BITS PILANI WILP
Usability Engineering- SZG547
Mid-Semester Question Paper - 2016-2017 Regular
BITS PILANI WILP
Data Mining - ISZC415 - Quiz 2 - BITS- WILP
Data Mining - ISZC415 - Quiz 2
BITS- WILP - MTEC
1. The entropy of a fair coin toss is:
Select one:
a. 0.25
b. 0.5
c. 1
d. 0
Ans: c. 1
2. A quiz question had names of 10 algorithms of which the student
had to select only the classification algorithms. A student
identified 7 of them as classification algorithms. During evaluation
it was found that 5 of the algorithms identified by the student were
indeed classification algorithms. The student was unable to identify
2 other classification algorithms in the list.
The F-score is:
Select one:
a. 0.59
b. 0.41
c. 0.69
d. 0.71
Ans: d. 0.71
3. Hash tree is created from:
Select one:
a. transactions
b. frequent itemsets
c. strong rules
d. candidate itemsets
Ans: d. candidate itemsets
4. The following data is about a poll that occurred in 3 states. In
state1, 50% of voters support Party1, in state2, 60% of the voters
support Party1, and in state3, 35% of the voters support Party1. Of
the total population of the three states, 40% live in state1, 25%
live in state2, and 35% live in state3. Given that a voter supports
Party1, what is the probability that he lives in state2?
Select one:
a. 0.52
b. 0.42
c. 0.32
d. 0.22
Ans: c. 0.32
5. A quiz question had names of 6 algorithms of which the student had
to select only the classification algorithms. A student identified 3
of them as classification algorithms. During evaluation it was found
that 2 of the algorithms identified by the student were indeed
classification algorithms. The student was unable to identify 2 other
classification algorithms in the list.
The recall is:
Select one:
a. 0.33
b. 0.5
c. 0.73
d. 0.6
Ans: b. 0.5
6. The table below shows marks in math (x) and marks in statistics
(y).
What is the value of the slope (m) of simple regression line ?
Select one:
a. 0.744
b. 0.644
c. 0.444
d. 0.544
Ans: b. 0.644
7. In case an item occurs N times in a single transaction, the
support count of that item:
Select one:
a. zero times
b. is counted only once
c. is counted N times
d. is counted threshold times
Ans: b. is counted only once
8. In association analysis, confidence measures certainty of the
rule.
Select one:
a. True
b. False
Ans: a. True
9. Decision tree pruning is done to prevent under-fitting the data.
Select one:
a. True
b. False
Ans: b. False
10. A decision tree is split on the attribute with highest Gini
Index.
Select one:
a. True
b. False
Ans: b. False
11. Gini index cannot be used to make a ternary split on an attribute
in decision tree classification.
Select one:
a. True
b. False
Ans: b. False
12. Decision tree splitting decision can be made based upon
information gain of the attributes, but not based upon entropy of the
attributes.
Select one:
a. True
b. False
Ans: b. False
13. Laplace smoothing is applied in Naive Bayes spam classifier
because it prevents the conditional probability from becoming zero if
some words are not present in the sample.
Select one:
a. True
b. False
Ans: a. True
14. In association analysis, support is a symmetric measure of
associations.
Select one:
a. True
b. False
Ans: a. True
15. In classification, we evaluate the performance of a classifier on
training data
Select one:
a. True
b. False
Ans: b. False
Data Mining - ZC415 - Mid-Semester Question Paper - 2017 Regular BITS PILANI WILP
Data Mining (ZC415)
Mid-Semester Question Paper - 2016-2017 Regular
BITS PILANI WILP
Information Retrieval - SSZG537 - Mid-Semester Question Paper - 2016-2017 Regular BITS PILANI WILP
Information Retrieval (SSZG537)
Mid-Semester Question Paper - 2016-2017 Regular
BITS PILANI WILP
Artificial Intelligence ZC444 Mid-Semester Question Paper - 2016-2017 Regular BITS PILANI WILP
Artificial Intelligence ZC444
Mid-Semester Question Paper - 2016-2017 Regular
BITS PILANI WILP
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