MCQOPTIONS
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MCQOPTIONS
This section includes 607 Mcqs, each offering curated multiple-choice questions to sharpen your Computer Science Engineering (CSE) knowledge and support exam preparation. Choose a topic below to get started.
| 401. |
To determine association rules from frequent item sets |
| A. | only minimum confidence needed |
| B. | neither support not confidence needed |
| C. | both minimum support and confidence are needed |
| D. | minimum support is needed |
| Answer» D. minimum support is needed | |
| 402. |
Clustering is ___________ and is example of ____________learning |
| A. | predictive and supervised |
| B. | predictive and unsupervised |
| C. | descriptive and supervised |
| D. | descriptive and unsupervised |
| Answer» E. | |
| 403. |
If an item set ‘XYZ’ is a frequent item set, then all subsets of that frequent item set are |
| A. | undefined |
| B. | not frequent |
| C. | frequent |
| D. | can not say |
| Answer» D. can not say | |
| 404. |
The apriori property means |
| A. | if a set cannot pass a test, its supersets will also fail the same test |
| B. | to decrease the efficiency, do level-wise generation of frequent item sets |
| C. | to improve the efficiency, do level-wise generation of frequent item sets d. |
| D. | if a set can pass a test, its supersets will fail the same test |
| Answer» B. to decrease the efficiency, do level-wise generation of frequent item sets | |
| 405. |
In a Rule based classifier, If there is a rule for each combination of attribute values, what do you called that rule set R |
| A. | exhaustive |
| B. | inclusive |
| C. | comprehensive |
| D. | mutually exclusive |
| Answer» B. inclusive | |
| 406. |
Which Association Rule would you prefer |
| A. | high support and medium confidence |
| B. | high support and low confidence |
| C. | low support and high confidence |
| D. | low support and low confidence |
| Answer» D. low support and low confidence | |
| 407. |
Which statement is true about neural network and linear regression models? |
| A. | both techniques build models whose output is determined by a linear sum of weighted input attribute values |
| B. | the output of both models is a categorical attribute value |
| C. | both models require numeric attributes to range between 0 and 1 |
| D. | both models require input attributes to be numeric |
| Answer» E. | |
| 408. |
A good clustering method will produce high quality clusters with |
| A. | high inter class similarity |
| B. | low intra class similarity |
| C. | high intra class similarity |
| D. | no inter class similarity |
| Answer» D. no inter class similarity | |
| 409. |
Frequent item sets is |
| A. | superset of only closed frequent item sets |
| B. | superset of only maximal frequent item sets |
| C. | subset of maximal frequent item sets |
| D. | superset of both closed frequent item sets and maximal frequent item sets |
| Answer» E. | |
| 410. |
The number of iterations in apriori ___________ Select one: a. b. c. d. |
| A. | increases with the size of the data |
| B. | decreases with the increase in size of the data |
| C. | increases with the size of the maximum frequent set |
| D. | decreases with increase in size of the maximum frequent set |
| Answer» D. decreases with increase in size of the maximum frequent set | |
| 411. |
This clustering algorithm terminates when mean values computed for the current iteration of the algorithm are identical to the computed mean values for the previous iteration |
| A. | k-means clustering |
| B. | conceptual clustering |
| C. | expectation maximization |
| D. | agglomerative clustering |
| Answer» B. conceptual clustering | |
| 412. |
Given that we can select the same feature multiple times during the recursive partitioning of the input space, is it always possible to achieve 100% accuracy on the training data (given that we allow for trees to grow to their maximum size) when building decision trees? |
| A. | yes |
| B. | no |
| Answer» C. | |
| 413. |
Which of the following classifications would best suit the student performance classification systems? |
| A. | if...then... analysis |
| B. | market-basket analysis |
| C. | regression analysis |
| D. | cluster analysis |
| Answer» B. market-basket analysis | |
| 414. |
What is the approach of basic algorithm for decision tree induction? |
| A. | greedy |
| B. | top down |
| C. | procedural |
| D. | step by step |
| Answer» B. top down | |
| 415. |
Which one of these is a tree based learner? |
| A. | rule based |
| B. | bayesian belief network |
| C. | bayesian classifier |
| D. | random forest |
| Answer» E. | |
| 416. |
Which one of these is not a tree based learner? |
| A. | cart |
| B. | id3 |
| C. | bayesian classifier |
| D. | random forest |
| Answer» D. random forest | |
| 417. |
The distance between two points calculated using Pythagoras theorem is |
| A. | supremum distance |
| B. | eucledian distance |
| C. | linear distance |
| D. | manhattan distance |
| Answer» C. linear distance | |
| 418. |
The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent,from being considered for counting support |
| A. | partitioning |
| B. | candidate generation |
| C. | itemset eliminations |
| D. | pruning |
| Answer» E. | |
| 419. |
Hierarchical agglomerative clustering is typically visualized as? |
| A. | dendrogram |
| B. | binary trees |
| C. | block diagram |
| D. | graph |
| Answer» B. binary trees | |
| 420. |
Which of the following algorithm comes under the classification |
| A. | apriori |
| B. | brute force |
| C. | dbscan |
| D. | k-nearest neighbor |
| Answer» E. | |
| 421. |
The most general form of distance is |
| A. | manhattan |
| B. | eucledian |
| C. | mean |
| D. | minkowski |
| Answer» C. mean | |
| 422. |
KDD represents extraction of |
| A. | data |
| B. | knowledge |
| C. | rules |
| D. | model |
| Answer» C. rules | |
| 423. |
Which statement is true about the K-Means algorithm? |
| A. | the output attribute must be cateogrical |
| B. | all attribute values must be categorical |
| C. | all attributes must be numeric |
| D. | attribute values may be either categorical or numeric |
| Answer» D. attribute values may be either categorical or numeric | |
| 424. |
his clustering approach initially assumes that each data instance represents a single cluster. |
| A. | expectation maximization |
| B. | k-means clustering |
| C. | agglomerative clustering |
| D. | conceptual clustering |
| Answer» D. conceptual clustering | |
| 425. |
Attribute selection measures are also known as splitting rules. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 426. |
When the number of classes is large Gini index is not a good choice. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 427. |
Gini index does not favour equal sized partitions. |
| A. | true |
| B. | false |
| Answer» C. | |
| 428. |
The gini index is not biased towards multivalued attributed. |
| A. | true |
| B. | false |
| Answer» C. | |
| 429. |
Gain ratio tends to prefer unbalanced splits in which one partition is much smaller than the other |
| A. | true |
| B. | false |
| Answer» B. false | |
| 430. |
Multivariate split is where the partitioning of tuples is based on a combination of attributes rather than on a single attribute. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 431. |
Which of the following sentences are correct in reference to Information gain? a. It is biased towards single-valued attributes b. It is biased towards multi-valued attributes c. ID3 makes use of information gain d. The approact used by ID3 is greedy |
| A. | a and b |
| B. | a and d |
| C. | b, c and d |
| D. | all of the above |
| Answer» D. all of the above | |
| 432. |
What is gini index? |
| A. | it is a type of index structure |
| B. | it is a measure of purity |
| C. | both options except none |
| D. | none of the options |
| Answer» C. both options except none | |
| 433. |
What are tree based classifiers? |
| A. | classifiers which form a tree with each attribute at one level |
| B. | classifiers which perform series of condition checking with one attribute at a time |
| C. | both options except none |
| D. | none of the options |
| Answer» D. none of the options | |
| 434. |
Choose the correct statement with respect to ‘confidence’ metric in association rules |
| A. | it is the conditional probability that a randomly selected transaction will include all the items in the consequent given that the transaction includes all the items in the antecedent. |
| B. | a high value of confidence suggests a weak association rule |
| C. | it is the probability that a randomly selected transaction will include all the items in the consequent as well as all the items in the antecedent. |
| D. | confidence is not measured in terms of (estimated) conditional probability. |
| Answer» B. a high value of confidence suggests a weak association rule | |
| 435. |
How can we best represent ‘support’ for the following association rule: “If X and Y, then Zâ€. |
| A. | {x,y}/(total number of transactions) |
| B. | {z}/(total number of transactions) |
| C. | {z}/{x,y} |
| D. | {x,y,z}/(total number of transactions) |
| Answer» D. {x,y,z}/(total number of transactions) | |
| 436. |
Problem in multi regression is ? |
| A. | multicollinearity |
| B. | overfitting |
| C. | both multicollinearity & overfitting |
| D. | underfitting |
| Answer» D. underfitting | |
| 437. |
If X and Y in a regression model are totally unrelated, |
| A. | the correlation coefficient would be -1 |
| B. | the coefficient of determination would be 0 |
| C. | the coefficient of determination would be 1 |
| D. | the sse would be 0 |
| Answer» C. the coefficient of determination would be 1 | |
| 438. |
Which of the following statements are true for a design matrix X ∈ Rn×d with d > n? (The rows are n sample points and the columns represent d features.) |
| A. | least-squares linear regression computes the weights w = (xtx)−1 xty |
| B. | the sample points are linearly separable |
| C. | x has exactly d − n eigenvectors with eigenvalue zero |
| D. | at least one principal component direction is orthogonal to a hyperplane that contains all the sample points |
| Answer» E. | |
| 439. |
In terms of bias and variance. Which of the following is true when you fit degree 2 polynomial? |
| A. | bias will be high, variance will be high |
| B. | bias will be low, variance will be high |
| C. | bias will be high, variance will be low |
| D. | bias will be low, variance will be low |
| Answer» D. bias will be low, variance will be low | |
| 440. |
Suppose that we have N independent variables (X1,X2… Xn) and dependent variable is Y. Now Imagine that you are applying linear regression by fitting the best fit line using least square error on this data. You found that correlation coefficient for one of it’s variable(Say X1) with Y is 0.95. |
| A. | relation between the x1 and y is weak |
| B. | relation between the x1 and y is strong |
| C. | relation between the x1 and y is neutral |
| D. | correlation can’t judge the relationship |
| Answer» C. relation between the x1 and y is neutral | |
| 441. |
Which of the following indicates the fundamental of least squares? |
| A. | arithmetic mean should be maximized |
| B. | arithmetic mean should be zero |
| C. | arithmetic mean should be neutralized |
| D. | arithmetic mean should be minimized |
| Answer» E. | |
| 442. |
Regarding bias and variance, which of the following statements are true? (Here ‘high’ and ‘low’ are relative to the ideal model. (i) Models which overfit are more likely to have high bias (ii) Models which overfit are more likely to have low bias (iii) Models which overfit are more likely to have high variance (iv) Models which overfit are more likely to have low variance |
| A. | (i) and (ii) |
| B. | (ii) and (iii) |
| C. | (iii) and (iv) |
| D. | none of these |
| Answer» C. (iii) and (iv) | |
| 443. |
Suppose, you got a situation where you find that your linear regression model is under fitting the data. In such situation which of the following options would you consider? |
| A. | you will add more features |
| B. | you will remove some features |
| C. | all of the above |
| D. | none of the above |
| Answer» B. you will remove some features | |
| 444. |
The selling price of a house depends on many factors. For example, it depends on the number of bedrooms, number of kitchen, number of bathrooms, the year the house was built, and the square footage of the lot. Given these factors, predicting the selling price of the house is an example of ____________ task. |
| A. | binary classification |
| B. | multilabel classification |
| C. | simple linear regression |
| D. | multiple linear regression |
| Answer» E. | |
| 445. |
In the regression equation Y = 75.65 + 0.50X, the intercept is |
| A. | 0.5 |
| B. | 75.65 |
| C. | 1 |
| D. | indeterminable |
| Answer» C. 1 | |
| 446. |
Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable? |
| A. | auc-roc |
| B. | accuracy |
| C. | logloss |
| D. | mean-squared-error |
| Answer» E. | |
| 447. |
Lasso can be interpreted as least-squares linear regression where |
| A. | weights are regularized with the l1 norm |
| B. | the weights have a gaussian prior |
| C. | weights are regularized with the l2 norm |
| D. | the solution algorithm is simpler |
| Answer» B. the weights have a gaussian prior | |
| 448. |
Which of the following methods do we use to best fit the data in Logistic Regression? |
| A. | least square error |
| B. | maximum likelihood |
| C. | jaccard distance |
| D. | both a and b |
| Answer» C. jaccard distance | |
| 449. |
Which of the following methods/methods do we use to find the best fit line for data in Linear Regression? |
| A. | least square error |
| B. | maximum likelihood |
| C. | logarithmic loss |
| D. | both a and b |
| Answer» B. maximum likelihood | |
| 450. |
Linear Regression is a _______ machine learning algorithm. |
| A. | supervised |
| B. | unsupervised |
| C. | semi-supervised |
| D. | can\t say |
| Answer» B. unsupervised | |