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.
| 301. |
Q25. Which of the following are advantages of stacking? 1) More robust model 2) better prediction 3) Lower time of execution |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 1 and 3 |
| D. | all of the above |
| Answer» B. 2 and 3 | |
| 302. |
Which of the following can be one of the steps in stacking? 1. Divide the training data into k folds 2. Train k models on each k-1 folds and get the out of fold predictions for remaining one fold 3. Divide the test data set in “k†folds and get individual fold predictions by different algorithms |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 1 and 3 |
| D. | all of above |
| Answer» B. 2 and 3 | |
| 303. |
Which of the following is the difference between stacking and blending? |
| A. | stacking has less stable cv compared to blending |
| B. | in blending, you create out of fold prediction |
| C. | stacking is simpler than blending |
| D. | none of these |
| Answer» E. | |
| 304. |
Suppose you are using stacking with n different machine learning algorithms with k folds on data. Which of the following is true about one level (m base models + 1 stacker) stacking? Note: Here, we are working on binary classification problem All base models are trained on all features You are using k folds for base models |
| A. | you will have only k features after the first stage |
| B. | you will have only m features after the first stage |
| C. | you will have k+m features after the first stage |
| D. | you will have k*n features after the first stage |
| Answer» C. you will have k+m features after the first stage | |
| 305. |
Which of the following is true about bagging? 1. Bagging can be parallel 2. The aim of bagging is to reduce bias not variance 3. Bagging helps in reducing overfitting |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 1 and 3 |
| D. | all of these |
| Answer» D. all of these | |
| 306. |
True or False: In boosting, individual base learners can be parallel. |
| A. | true |
| B. | false |
| Answer» C. | |
| 307. |
Below are the two ensemble models: 1. E1(M1, M2, M3) and 2. E2(M4, M5, M6) Above, Mx is the individual base models. Which of the following are more likely to choose if following conditions for E1 and E2 are given? E1: Individual Models accuracies are high but models are of the same type or in another term less diverse E2: Individual Models accuracies are high but they are of different types in another term high diverse in nature |
| A. | e1 |
| B. | e2 |
| C. | any of e1 and e2 |
| D. | none of these |
| Answer» C. any of e1 and e2 | |
| 308. |
Suppose, you have 2000 different models with their predictions and want to ensemble predictions of best x models. Now, which of the following can be a possible method to select the best x models for an ensemble? |
| A. | step wise forward selection |
| B. | step wise backward elimination |
| C. | both |
| D. | none of above |
| Answer» D. none of above | |
| 309. |
Suppose, you want to apply a stepwise forward selection method for choosing the best models for an ensemble model. Which of the following is the correct order of the steps? Note: You have more than 1000 models predictions 1. Add the models predictions (or in another term take the average) one by one in the ensemble which improves the metrics in the validation set. 2. Start with empty ensemble 3. Return the ensemble from the nested set of ensembles that has maximum performance on the validation set |
| A. | 1-2-3 |
| B. | 1-3-4 |
| C. | 2-1-3 |
| D. | none of above |
| Answer» E. | |
| 310. |
True or False: Dropout is computationally expensive technique w.r.t. bagging |
| A. | true |
| B. | false |
| Answer» C. | |
| 311. |
How is the model capacity affected with dropout rate (where model capacity means the ability of a neural network to approximate complex functions)? |
| A. | model capacity increases in increase in dropout rate |
| B. | model capacity decreases in increase in dropout rate |
| C. | model capacity is not affected on increase in dropout rate |
| D. | none of these |
| Answer» C. model capacity is not affected on increase in dropout rate | |
| 312. |
In machine learning, an algorithm (or learning algorithm) is said to be unstable if a small change in training data cause the large change in the learned classifiers. True or False: Bagging of unstable classifiers is a good idea |
| A. | true |
| B. | false |
| Answer» B. false | |
| 313. |
Suppose there are 25 base classifiers. Each classifier has error rates of e = 0.35. Suppose you are using averaging as ensemble technique. What will be the probabilities that ensemble of above 25 classifiers will make a wrong prediction? Note: All classifiers are independent of each other |
| A. | 0.05 |
| B. | 0.06 |
| C. | 0.07 |
| D. | 0.09 |
| Answer» C. 0.07 | |
| 314. |
In an election, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes. Which of the following ensemble method works similar to above-discussed election procedure? Hint: Persons are like base models of ensemble method. |
| A. | bagging |
| B. | boosting |
| C. | a or b |
| D. | none of these |
| Answer» B. boosting | |
| 315. |
Generally, an ensemble method works better, if the individual base models have ____________? Note: Suppose each individual base models have accuracy greater than 50%. |
| A. | less correlation among predictions |
| B. | high correlation among predictions |
| C. | correlation does not have any impact on ensemble output |
| D. | none of the above |
| Answer» B. high correlation among predictions | |
| 316. |
If you use an ensemble of different base models, is it necessary to tune the hyper parameters of all base models to improve the ensemble performance? |
| A. | yes |
| B. | no |
| C. | can’t say |
| Answer» C. can’t say | |
| 317. |
True or False: Ensemble of classifiers may or may not be more accurate than any of its individual model. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 318. |
Which of the following is / are true about weak learners used in ensemble model? 1. They have low variance and they don’t usually overfit 2. They have high bias, so they can not solve hard learning problems 3. They have high variance and they don’t usually overfit |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 3 |
| D. | none of these |
| Answer» B. 1 and 3 | |
| 319. |
True or False: Ensembles will yield bad results when there is significant diversity among the models. Note: All individual models have meaningful and good predictions. |
| A. | true |
| B. | false |
| Answer» C. | |
| 320. |
True or False: Ensemble learning can only be applied to supervised learning methods. |
| A. | true |
| B. | false |
| Answer» C. | |
| 321. |
Which of the following can be true for selecting base learners for an ensemble? 1. Different learners can come from same algorithm with different hyper parameters 2. Different learners can come from different algorithms 3. Different learners can come from different training spaces |
| A. | 1 |
| B. | 2 |
| C. | 1 and 3 |
| D. | 1, 2 and 3 |
| Answer» E. | |
| 322. |
Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability |
| A. | 1 and 3 |
| B. | 2 and 3 |
| C. | 1 and 2 |
| D. | 1, 2 and 3 |
| Answer» D. 1, 2 and 3 | |
| 323. |
What is true about an ensembled classifier? 1. Classifiers that are more “sure†can vote with more conviction 2. Classifiers can be more “sure†about a particular part of the space 3. Most of the times, it performs better than a single classifier |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 3 |
| D. | all of the above |
| Answer» E. | |
| 324. |
The F-test |
| A. | an omnibus test |
| B. | considers the reduction in error when moving from the complete model to the reduced model |
| C. | considers the reduction in error when moving from the reduced model to the complete model |
| D. | can only be conceptualized as a reduction in error |
| Answer» D. can only be conceptualized as a reduction in error | |
| 325. |
Increase in size of a convolutional kernel would necessarily increase the performance of a convolutional network. |
| A. | true |
| B. | false |
| Answer» C. | |
| 326. |
A 4-input neuron has weights 1, 2, 3 and 4. The transfer function is linear with the constant of proportionality being equal to 2. The inputs are 4, 10, 10 and 30 respectively. What will be the output? |
| A. | 238 |
| B. | 76 |
| C. | 248 |
| D. | 348 |
| Answer» E. | |
| 327. |
What are the steps for using a gradient descent algorithm? 1)Calculate error between the actual value and the predicted value 2)Reiterate until you find the best weights of network 3)Pass an input through the network and get values from output layer 4)Initialize random weight and bias 5)Go to each neurons which contributes to the error and change its respective values to reduce the error |
| A. | 1, 2, 3, 4, 5 |
| B. | 4, 3, 1, 5, 2 |
| C. | 3, 2, 1, 5, 4 |
| D. | 5, 4, 3, 2, 1 |
| Answer» C. 3, 2, 1, 5, 4 | |
| 328. |
 Given above is a description of a neural network. When does a neural network model become a deep learning model? |
| A. | when you add more hidden layers and increase depth of neural network |
| B. | when there is higher dimensionality of data |
| C. | when the problem is an image recognition problem |
| D. | when there is lower dimensionality of data |
| Answer» B. when there is higher dimensionality of data | |
| 329. |
Which of the following are correct statement(s) about stacking? 1. A machine learning model is trained on predictions of multiple machine learning models 2. A Logistic regression will definitely work better in the second stage as compared to other classification methods 3. First stage models are trained on full / partial feature space of training data |
| A. | 1 and 2 |
| B. | 2 and 3 |
| C. | 1 and 3 |
| D. | 1,2 and 3 |
| Answer» D. 1,2 and 3 | |
| 330. |
In which neural net architecture, does weight sharing occur? |
| A. | recurrent neural network |
| B. | convolutional neural network |
| C. | . fully connected neural network |
| D. | both a and b |
| Answer» E. | |
| 331. |
What is the sequence of the following tasks in a perceptron? Initialize weights of perceptron randomly Go to the next batch of dataset If the prediction does not match the output, change the weights For a sample input, compute an output |
| A. | 1, 4, 3, 2 |
| B. | 3, 1, 2, 4 |
| C. | 4, 3, 2, 1 |
| D. | 1, 2, 3, 4 |
| Answer» B. 3, 1, 2, 4 | |
| 332. |
In an election for the head of college, N candidates are competing against each other and people are voting for either of the candidates. Voters don’t communicate with each other while casting their votes.which of the following ensembles method works similar to the discussed elction Procedure? |
| A. | ??bagging |
| B. | boosting |
| C. | stacking |
| D. | randomization |
| Answer» B. boosting | |
| 333. |
What is back propagation? a) It is another name given to the curvy function in the perceptron b) It is the transmission of error back through the network to adjust the inputs c) It is the transmission of error back through the network to allow weights to be adjusted so that the network can learn d) None of the mentioned |
| A. | a |
| B. | b |
| C. | c |
| D. | b&c |
| Answer» D. b&c | |
| 334. |
Which of the following parameters can be tuned for finding good ensemble model in bagging based algorithms? 1. Max number of samples 2. Max features 3. Bootstrapping of samples 4. Bootstrapping of features |
| A. | 1 |
| B. | 2 |
| C. | 3&4 |
| D. | 1,2,3&4 |
| Answer» E. | |
| 335. |
The network that involves backward links from output to the input and hidden layers is called |
| A. | self organizing maps |
| B. | perceptrons |
| C. | recurrent neural network |
| D. | multi layered perceptron |
| Answer» D. multi layered perceptron | |
| 336. |
Which one of the following is not a major strength of the neural network approach? |
| A. | neural network learning algorithms are guaranteed to converge to an optimal solution |
| B. | neural networks work well with datasets containing noisy data |
| C. | neural networks can be used for both supervised learning and unsupervised clustering |
| D. | neural networks can be used for applications that require a time element to be included in the data |
| Answer» B. neural networks work well with datasets containing noisy data | |
| 337. |
Having multiple perceptrons can actually solve the XOR problem satisfactorily: this is because each perceptron can partition off a linear part of the space itself, and they can then combine their results. |
| A. | true – this works always, and these multiple perceptrons learn to classify even complex problems |
| B. | false – perceptrons are mathematically incapable of solving linearly inseparable functions, no matter what you do |
| C. | true – perceptrons can do this but are unable to learn to do it – they have to be explicitly hand-coded |
| D. | false – just having a single perceptron is enough |
| Answer» D. false – just having a single perceptron is enough | |
| 338. |
Neural Networks are complex ______________ with many parameters. |
| A. | linear functions |
| B. | nonlinear functions |
| C. | discrete functions |
| D. | exponential functions |
| Answer» B. nonlinear functions | |
| 339. |
Which of the following is an application of NN (Neural Network)? |
| A. | sales forecasting |
| B. | data validation |
| C. | risk management |
| D. | all of the mentioned |
| Answer» E. | |
| 340. |
What is back propagation? |
| A. | it is another name given to the curvy function in the perceptron |
| B. | it is the transmission of error back through the network to adjust the inputs |
| C. | it is the transmission of error back through the network to allow weights to be adjusted so that the network can learn |
| D. | none of the mentioned |
| Answer» B. it is the transmission of error back through the network to adjust the inputs | |
| 341. |
Which of the following option is / are correct regarding benefits of ensemble model? 1. Better performance 2. Generalized models 3. Better interpretability |
| A. | 1 and 3 |
| B. | 2 and 3 |
| C. | 1, 2 and 3 |
| D. | 1 and 2 |
| Answer» E. | |
| 342. |
In Naive Bayes equation P(C / X)= (P(X / C) *P(C) ) / P(X) which part considers "likelihood"? |
| A. | p(x/c) |
| B. | p(c/x) |
| C. | p(c) |
| D. | p(x) |
| Answer» B. p(c/x) | |
| 343. |
Skewness of Normal distribution is ___________ |
| A. | negative |
| B. | positive |
| C. | undefined |
| Answer» D. | |
| 344. |
How many terms are required for building a bayes model? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» D. 4 | |
| 345. |
How the entries in the full joint probability distribution can be calculated? |
| A. | using variables |
| B. | using information |
| C. | both using variables & information |
| D. | none of the mentioned |
| Answer» C. both using variables & information | |
| 346. |
Which of the following statements about Naive Bayes is incorrect? |
| A. | attributes are equally important. |
| B. | attributes are statistically dependent of one another given the class value. |
| C. | attributes are statistically independent of one another given the class value. |
| D. | attributes can be nominal or numeric |
| Answer» C. attributes are statistically independent of one another given the class value. | |
| 347. |
Given a rule of the form IF X THEN Y, rule confidence is defined as the conditional probability that Select one: |
| A. | y is false when x is known to be false. |
| B. | y is true when x is known to be true. |
| C. | x is true when y is known to be true |
| D. | x is false when y is known to be false. |
| Answer» C. x is true when y is known to be true | |
| 348. |
Consider the following dataset. x,y,z are the features and T is a class(1/0). Classify the test data (0,0,1) as values of x,y,z respectively. |
| A. | 0 |
| B. | 1 |
| C. | 0.1 |
| D. | 0.9 |
| Answer» C. 0.1 | |
| 349. |
Which of the following quantities are minimized directly or indirectly during parameter estimation in Gaussian distribution Model? |
| A. | negative log-likelihood |
| B. | log-liklihood |
| C. | cross entropy |
| D. | residual sum of square |
| Answer» B. log-liklihood | |
| 350. |
Give the correct Answer for following statements. 1. It is important to perform feature normalization before using the Gaussian kernel. 2. The maximum value of the Gaussian kernel is 1. |
| A. | 1 is true, 2 is false |
| B. | 1 is false, 2 is true |
| C. | 1 is true, 2 is true |
| D. | 1 is false, 2 is false |
| Answer» D. 1 is false, 2 is false | |