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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.
| 351. |
What is the actual number of independent parameters which need to be estimated in P dimensional Gaussian distribution model? |
| A. | p |
| B. | 2p |
| C. | p(p+1)/2 |
| D. | p(p+3)/2 |
| Answer» E. | |
| 352. |
What is the naïve assumption in a Naïve Bayes Classifier. |
| A. | all the classes are independent of each other |
| B. | all the features of a class are independent of each other |
| C. | the most probable feature for a class is the most important feature to be cinsidered for classification |
| D. | all the features of a class are conditionally dependent on each other |
| Answer» E. | |
| 353. |
The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car |
| A. | 0.0398 |
| B. | 0.0389 |
| C. | 0.0368 |
| D. | 0.0396 |
| Answer» E. | |
| 354. |
With Bayes theorem the probability of hypothesis H¾ specified by P(H) ¾ is referred to as |
| A. | a conditional probability |
| B. | an a priori probability |
| C. | a bidirectional probability |
| D. | a posterior probability |
| Answer» C. a bidirectional probability | |
| 355. |
what is Feature scaling done before applying K-Mean algorithm? |
| A. | in distance calculation it will give the same weights for all features |
| B. | you always get the same clusters. if you use or don\t use feature scaling |
| C. | in manhattan distance it is an important step but in euclidian it is not |
| D. | none of these |
| Answer» B. you always get the same clusters. if you use or don\t use feature scaling | |
| 356. |
Which Statement is not true statement. |
| A. | k-means clustering is a linear clustering algorithm. |
| B. | k-means clustering aims to partition n observations into k clusters |
| C. | k-nearest neighbor is same as k-means |
| D. | k-means is sensitive to outlier |
| Answer» D. k-means is sensitive to outlier | |
| 357. |
The maximum likelihood method can be used to explore relationships among more diverse sequences, conditions that are not well handled by maximum parsimony methods. |
| A. | true |
| B. | false |
| C. | - |
| D. | - |
| Answer» B. false | |
| 358. |
The main disadvantage of maximum likelihood methods is that they are _____ |
| A. | mathematically less folded |
| B. | mathematically less complex |
| C. | mathematically less complex |
| D. | computationally intense |
| Answer» E. | |
| 359. |
Suppose we would like to perform clustering on spatial data such as the geometrical locations of houses. We wish to produce clusters of many different sizes and shapes. Which of the following methods is the most appropriate? |
| A. | decision trees |
| B. | density-based clustering |
| C. | model-based clustering |
| D. | k-means clustering |
| Answer» C. model-based clustering | |
| 360. |
High entropy means that the partitions in classification are |
| A. | pure |
| B. | not pure |
| C. | useful |
| D. | useless |
| Answer» C. useful | |
| 361. |
Hierarchical clustering is slower than non-hierarchical clustering? |
| A. | true |
| B. | false |
| C. | depends on data |
| D. | cannot say |
| Answer» B. false | |
| 362. |
In which of the following cases will K-Means clustering fail to give good results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 2 and 4 |
| D. | 1, 2 and 4 |
| Answer» E. | |
| 363. |
Which of the following metrics, do we have for finding dissimilarity between two clusters in hierarchical clustering? 1. Single-link 2. Complete-link 3. Average-link |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 3 |
| D. | 1, 2 and 3 |
| Answer» E. | |
| 364. |
The K-means algorithm: |
| A. | requires the dimension of the feature space to be no bigger than the number of samples |
| B. | has the smallest value of the objective function when k = 1 |
| C. | minimizes the within class variance for a given number of clusters |
| D. | converges to the global optimum if and only if the initial means are chosen as some of the samples themselves |
| Answer» D. converges to the global optimum if and only if the initial means are chosen as some of the samples themselves | |
| 365. |
You've just finished training a decision tree for spam classification, and it is getting abnormally bad performance on both your training and test sets. You know that your implementation has no bugs, so what could be causing the problem? |
| A. | your decision trees are too shallow. |
| B. | you need to increase the learning rate. |
| C. | you are overfitting. |
| D. | incorrect data |
| Answer» B. you need to increase the learning rate. | |
| 366. |
Which one of the following is the main reason for pruning a Decision Tree? |
| A. | to save computing time during testing |
| B. | to save space for storing the decision tree |
| C. | to make the training set error smaller |
| D. | to avoid overfitting the training set |
| Answer» E. | |
| 367. |
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 Select one: |
| A. | k-means clustering |
| B. | conceptual clustering |
| C. | expectation maximization |
| D. | agglomerative clustering |
| Answer» B. conceptual clustering | |
| 368. |
In which of the following cases will K-means clustering fail to give good results? 1) Data points with outliers 2) Data points with different densities 3) Data points with nonconvex shapes |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 1, 2, and 3?? |
| D. | 1 and 3 |
| Answer» D. 1 and 3 | |
| 369. |
Which of the following statement is true about k-NN algorithm? 1) k-NN performs much better if all of the data have the same scale 2) k-NN works well with a small number of input variables (p), but struggles when the number of inputs is very large 3) k-NN makes no assumptions about the functional form of the problem being solved |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | only 1 |
| D. | 1,2 and 3 |
| Answer» E. | |
| 370. |
Which of the following can act as possible termination conditions in K-Means? 1. For a fixed number of iterations. 2. Assignment of observations to clusters does not change between iterations. Except for cases with a bad local minimum. 3. Centroids do not change between successive iterations. 4. Terminate when RSS falls below a threshold. |
| A. | 1, 3 and 4 |
| B. | 1, 2 and 3 |
| C. | 1, 2 and 4 |
| D. | 1,2,3,4 |
| Answer» E. | |
| 371. |
Which statement is true about the K-Means algorithm? Select one: |
| 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 | |
| 372. |
A company has build a kNN classifier that gets 100% accuracy on training data. When they deployed this model on client side it has been found that the model is not at all accurate. Which of the following thing might gone wrong? Note: Model has successfully deployed and no technical issues are found at client side except the model performance |
| A. | it is probably a overfitted model |
| B. | ??it is probably a underfitted model |
| C. | ??can’t say |
| D. | wrong client data |
| Answer» B. ??it is probably a underfitted model | |
| 373. |
Which of the following is true about Manhattan distance? |
| A. | it can be used for continuous variables |
| B. | it can be used for categorical variables |
| C. | it can be used for categorical as well as continuous |
| D. | it can be used for constants |
| Answer» B. it can be used for categorical variables | |
| 374. |
Decision Tree is |
| A. | flow-chart |
| B. | structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label |
| C. | both a & b |
| D. | class of instance |
| Answer» D. class of instance | |
| 375. |
Tree/Rule based classification algorithms generate ... rule to perform the classification. |
| A. | if-then. |
| B. | while. |
| C. | do while |
| D. | switch. |
| Answer» B. while. | |
| 376. |
What is true about K-Mean Clustering? 1. K-means is extremely sensitive to cluster center initializations 2. Bad initialization can lead to Poor convergence speed 3. Bad initialization can lead to bad overall clustering |
| A. | 1 and 3 |
| B. | 1 and 2 |
| C. | 2 and 3 |
| D. | 1, 2 and 3 |
| Answer» E. | |
| 377. |
How to select best hyperparameters in tree based models? |
| A. | measure performance over training data |
| B. | measure performance over validation data |
| C. | both of these |
| D. | random selection of hyper parameters |
| Answer» C. both of these | |
| 378. |
Which of the following option is true about k-NN algorithm? |
| A. | it can be used for classification |
| B. | ??it can be used for regression |
| C. | ??it can be used in both classification and regression?? |
| D. | not useful in ml algorithm |
| Answer» D. not useful in ml algorithm | |
| 379. |
A database has 5 transactions. Of these, 4 transactions include milk and bread. Further, of the given 4 transactions, 2 transactions include cheese. Find the support percentage for the following association rule “if milk and bread are purchased, then cheese is also purchasedâ€. |
| A. | 0.4 |
| B. | 0.6 |
| C. | 0.8 |
| D. | 0.42 |
| Answer» E. | |
| 380. |
8 observations are clustered into 3 clusters using K-Means clustering algorithm. After first iteration clusters, C1, C2, C3 has following observations: C1: {(2,2), (4,4), (6,6)} C2: {(0,4), (4,0),(2,5)} C3: {(5,5), (9,9)} What will be the cluster centroids if you want to proceed for second iteration? |
| A. | ??c1: (4,4), c2: (2,2), c3: (7,7) |
| B. | c1: (6,6), c2: (4,4), c3: (9,9) |
| C. | ??c1: (2,2), c2: (0,0), c3: (5,5) |
| D. | c1: (4,4), c2: (3,3), c3: (7,7) |
| Answer» D. c1: (4,4), c2: (3,3), c3: (7,7) | |
| 381. |
What is Decision Tree? |
| A. | flow-chart |
| B. | structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label |
| C. | flow-chart like structure in which internal node represents test on an attribute, each branch represents outcome of test and each leaf node represents class label |
| D. | none of the above |
| Answer» E. | |
| 382. |
which of the following cases will K-Means clustering give poor results? 1. Data points with outliers 2. Data points with different densities 3. Data points with round shapes 4. Data points with non-convex shapes |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 2 and 4 |
| D. | 1, 2 and 4 |
| Answer» D. 1, 2 and 4 | |
| 383. |
Which Statement is not true statement. |
| A. | k-means clustering is a linear clustering algorithm. |
| B. | k-means clustering aims to partition n observations into k clusters |
| C. | k-nearest neighbor is same as k-means |
| D. | k-means is sensitive to outlier |
| Answer» C. k-nearest neighbor is same as k-means | |
| 384. |
Given a frequent itemset L, If |L| = k, then there are |
| A. | 2k – 1 candidate association rules |
| B. | 2k candidate association rules |
| C. | 2k – 2 candidate association rules |
| D. | 2k -2 candidate association rules |
| Answer» D. 2k -2 candidate association rules | |
| 385. |
What is the final resultant cluster size in Divisive algorithm, which is one of the hierarchical clustering approaches? |
| A. | zero |
| B. | three |
| C. | singleton |
| D. | two |
| Answer» D. two | |
| 386. |
The probability that a person owns a sports car given that they subscribe to automotive magazine is 40%. We also know that 3% of the adult population subscribes to automotive magazine. The probability of a person owning a sports car given that they don’t subscribe to automotive magazine is 30%. Use this information to compute the probability that a person subscribes to automotive magazine given that they own a sports car |
| A. | 0.0368 |
| B. | 0.0396 |
| C. | 0.0389 |
| D. | 0.0398 |
| Answer» C. 0.0389 | |
| 387. |
In Apriori algorithm, if 1 item-sets are 100, then the number of candidate 2 item-sets are |
| A. | 100 |
| B. | 200 |
| C. | 4950 |
| D. | 5000 |
| Answer» D. 5000 | |
| 388. |
Time Complexity of k-means is given by |
| A. | o(mn) |
| B. | o(tkn) |
| C. | o(kn) |
| D. | o(t2kn) |
| Answer» C. o(kn) | |
| 389. |
Suppose on performing reduced error pruning, we collapsed a node and observed an improvement in the prediction accuracy on the validation set. Which among the following statements are possible in light of the performance improvement observed? (a) The collapsed node helped overcome the effect of one or more noise affected data points in the training set (b) The validation set had one or more noise affected data points in the region corresponding to the collapsed node (c) The validation set did not have any data points along at least one of the collapsed branches (d) The validation set did have data points adversely affected by the collapsed node |
| A. | a and b |
| B. | a and d |
| C. | b, c and d |
| D. | all of the above |
| Answer» E. | |
| 390. |
Having built a decision tree, we are using reduced error pruning to reduce the size of the tree. We select a node to collapse. For this particular node, on the left branch, there are 3 training data points with the following outputs: 5, 7, 9.6 and for the right branch, there are four training data points with the following outputs: 8.7, 9.8, 10.5, 11. What were the original responses for data points along the two branches (left & right respectively) and what is the new response after collapsing the node? |
| A. | 10.8, 13.33, 14.48 |
| B. | 10.8, 13.33, 12.06 |
| C. | 7.2, 10, 8.8 |
| D. | 7.2, 10, 8.6 |
| Answer» D. 7.2, 10, 8.6 | |
| 391. |
Which among the following statements best describes our approach to learning decision trees |
| A. | identify the best partition of the input space and response per partition to minimise sum of squares error |
| B. | identify the best approximation of the above by the greedy approach (to identifying the partitions) |
| C. | identify the model which gives the best performance using the greedy approximation (option (b)) with the smallest partition scheme |
| D. | identify the model which gives performance close to the best greedy approximation performance (option (b)) with the smallest partition scheme |
| Answer» E. | |
| 392. |
To control the size of the tree, we need to control the number of regions. One approach to do this would be to split tree nodes only if the resultant decrease in the sum of squares error exceeds some threshold. For the described method, which among the following are true? (a) It would, in general, help restrict the size of the trees (b) It has the potential to affect the performance of the resultant regression/classification model (c) It is computationally infeasible |
| A. | a and b |
| B. | a and d |
| C. | b, c and d |
| D. | all of the above |
| Answer» B. a and d | |
| 393. |
Which of the following properties are characteristic of decision trees? (a) High bias (b) High variance (c) Lack of smoothness of prediction surfaces (d) Unbounded parameter set |
| A. | a and b |
| B. | a and d |
| C. | b, c and d |
| D. | all of the above |
| Answer» D. all of the above | |
| 394. |
Assume that you are given a data set and a neural network model trained on the data set. You are asked to build a decision tree model with the sole purpose of understanding/interpreting the built neural network model. In such a scenario, which among the following measures would you concentrate most on optimising? |
| A. | accuracy of the decision tree model on the given data set |
| B. | f1 measure of the decision tree model on the given data set |
| C. | fidelity of the decision tree model, which is the fraction of instances on which the neural network and the decision tree give the same output |
| D. | comprehensibility of the decision tree model, measured in terms of the size of the corresponding rule set |
| Answer» D. comprehensibility of the decision tree model, measured in terms of the size of the corresponding rule set | |
| 395. |
Which of the following sentences are true? |
| A. | in pre-pruning a tree is \pruned\ by halting its construction early |
| B. | a pruning set of class labelled tuples is used to estimate cost complexity |
| C. | the best pruned tree is the one that minimizes the number of encoding bits |
| D. | all of the above |
| Answer» E. | |
| 396. |
What are two steps of tree pruning work? |
| A. | pessimistic pruning and optimistic pruning |
| B. | postpruning and prepruning |
| C. | cost complexity pruning and time complexity pruning |
| D. | none of the options |
| Answer» C. cost complexity pruning and time complexity pruning | |
| 397. |
How will you counter over-fitting in decision tree? |
| A. | by pruning the longer rules |
| B. | by creating new rules |
| C. | both by pruning the longer rules’ and ‘ by creating new rules’ |
| D. | none of the options |
| Answer» B. by creating new rules | |
| 398. |
What does K refers in the K-Means algorithm which is a non-hierarchical clustering approach? |
| A. | complexity |
| B. | fixed value |
| C. | no of iterations |
| D. | number of clusters |
| Answer» E. | |
| 399. |
Classification rules are extracted from _____________ |
| A. | decision tree |
| B. | root node |
| C. | branches |
| D. | siblings |
| Answer» B. root node | |
| 400. |
If {A,B,C,D} is a frequent itemset, candidate rules which is not possible is |
| A. | c –> a |
| B. | d –>abcd |
| C. | a –> bc |
| D. | b –> adc |
| Answer» C. a –> bc | |