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.
| 251. |
Naive Bayes classifiers is                             Learning |
| A. | supervised |
| B. | unsupervised |
| C. | both |
| D. | none |
| Answer» B. unsupervised | |
| 252. |
Naive Bayes classifiers are a collection ------------------of algorithms |
| A. | classification |
| B. | clustering |
| C. | regression |
| D. | all |
| Answer» B. clustering | |
| 253. |
Suppose you plotted a scatter plot between the residuals and predicted values in linear regression and you found that there is a relationship between them. Which of the following conclusion do you make about this situation? |
| A. | since the there is a relationship means our model is not good |
| B. | since the there is a relationship means our model is good |
| C. | cant say |
| D. | none of these |
| Answer» B. since the there is a relationship means our model is good | |
| 254. |
Which of the following statement is true about outliers in Linear regression? |
| A. | linear regression is sensitive to outliers |
| B. | linear regression is not sensitive to outliers |
| C. | cant say |
| D. | none of these |
| Answer» B. linear regression is not sensitive to outliers | |
| 255. |
Overfitting is more likely when you have huge amount of data to train? |
| A. | true |
| B. | false |
| Answer» C. | |
| 256. |
Which of the following is true about Residuals ? |
| A. | lower is better |
| B. | higher is better |
| C. | a or b depend on the situation |
| D. | none of these |
| Answer» B. higher is better | |
| 257. |
Which of the following 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 | |
| 258. |
It is possible to design a Linear regression algorithm using a neural network? |
| A. | true |
| B. | false |
| Answer» B. false | |
| 259. |
Linear Regression is a supervised machine learning algorithm. |
| A. | true |
| B. | false |
| Answer» B. false | |
| 260. |
In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to |
| A. | (x-intercept, slope) |
| B. | (slope, x-intercept) |
| C. | (y-intercept, slope) |
| D. | (slope, y-intercept) |
| Answer» D. (slope, y-intercept) | |
| 261. |
In syntax of linear model lm(formula,data,..), data refers to |
| A. | matrix |
| B. | vector |
| C. | array |
| D. | list |
| Answer» C. array | |
| 262. |
Function used for linear regression in R is |
| A. | lm(formula, data) |
| B. | lr(formula, data) |
| C. | lrm(formula, data) |
| D. | regression.linear(formula, data) |
| Answer» B. lr(formula, data) | |
| 263. |
In a simple linear regression model (One independent variable), If we change the input variable by 1 unit. How much output variable will change? |
| A. | by 1 |
| B. | no change |
| C. | by intercept |
| D. | by its slope |
| Answer» E. | |
| 264. |
How many coefficients do you need to estimate in a simple linear regression model (One independent variable)? |
| A. | 1 |
| B. | 2 |
| C. | 3 |
| D. | 4 |
| Answer» C. 3 | |
| 265. |
Which of the following metrics can be used for evaluating regression models?i) R Squaredii) Adjusted R Squarediii) F Statisticsiv) RMSE / MSE / MAE |
| A. | ii and iv |
| B. | i and ii |
| C. | ii, iii and iv |
| D. | i, ii, iii and iv |
| Answer» E. | |
| 266. |
If Linear regression model perfectly first i.e., train error is zero, then |
| A. | test error is also always zero |
| B. | test error is non zero |
| C. | couldnt comment on test error |
| D. | test error is equal to train error |
| Answer» D. test error is equal to train error | |
| 267. |
             adopts a dictionary-oriented approach, associating to each category label a progressive integer number. |
| A. | labelencoder class |
| B. | labelbinarizer class |
| C. | dictvectorizer |
| D. | featurehasher |
| Answer» B. labelbinarizer class | |
| 268. |
In many classification problems, the target             is made up of categorical labels which cannot immediately be processed by any algorithm. |
| A. | random_state |
| B. | dataset |
| C. | test_size |
| D. | all above |
| Answer» C. test_size | |
| 269. |
The parameter            allows specifying the percentage of elements to put into the test/training set |
| A. | test_size |
| B. | training_size |
| C. | all above |
| D. | none of these |
| Answer» D. none of these | |
| 270. |
What are common feature selection methods in regression task? |
| A. | correlation coefficient |
| B. | greedy algorithms |
| C. | all above |
| D. | none of these |
| Answer» D. none of these | |
| 271. |
Can a model trained for item based similarity also choose from a given set of items? |
| A. | yes |
| B. | no |
| Answer» B. no | |
| 272. |
What is PCA, KPCA and ICA used for? |
| A. | principal components analysis |
| B. | kernel based principal component analysis |
| C. | independent component analysis |
| D. | all above |
| Answer» E. | |
| 273. |
What would you do in PCA to get the same projection as SVD? |
| A. | transform data to zero mean |
| B. | transform data to zero median |
| C. | not possible |
| D. | none of these |
| Answer» B. transform data to zero median | |
| 274. |
A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Which of the following statement is true in following case? |
| A. | feature f1 is an example of nominal variable. |
| B. | feature f1 is an example of ordinal variable. |
| C. | it doesnt belong to any of the above category. |
| D. | both of these |
| Answer» C. it doesnt belong to any of the above category. | |
| 275. |
              performs a PCA with non-linearly separable data sets. |
| A. | sparsepca |
| B. | kernelpca |
| C. | svd |
| D. | none of the mentioned |
| Answer» C. svd | |
| 276. |
Which of the following selects only a subset of features belonging to a certain percentile |
| A. | selectpercentile |
| B. | featurehasher |
| C. | selectkbest |
| D. | all above |
| Answer» B. featurehasher | |
| 277. |
There are also many univariate methods that can be used in order to select the best features according to specific criteria based on              . |
| A. | f-tests and p-values |
| B. | chi-square |
| C. | anova |
| D. | all above |
| Answer» B. chi-square | |
| 278. |
scikit-learn also provides a class for per- sample normalization, Normalizer. It can apply              to each element of a dataset |
| A. | max, l0 and l1 norms |
| B. | max, l1 and l2 norms |
| C. | max, l2 and l3 norms |
| D. | max, l3 and l4 norms |
| Answer» C. max, l2 and l3 norms | |
| 279. |
If you need a more powerful scaling feature, with a superior control on outliers and the possibility to select a quantile range, there's also the class              . |
| A. | robustscaler |
| B. | dictvectorizer |
| C. | labelbinarizer |
| D. | featurehasher |
| Answer» B. dictvectorizer | |
| 280. |
How it's possible to use a different placeholder through the parameter             . |
| A. | regression |
| B. | classification |
| C. | random_state |
| D. | missing_values |
| Answer» E. | |
| 281. |
              is much more difficult because it's necessary to determine a supervised strategy to train a model for each feature and, finally, to predict their value |
| A. | removing the whole line |
| B. | creating sub-model to predict those features |
| C. | using an automatic strategy to input them according to the other known values |
| D. | all above |
| Answer» C. using an automatic strategy to input them according to the other known values | |
| 282. |
What is Test set? |
| A. | test set is used to test the accuracy of the hypotheses generated by the learner. |
| B. | it is a set of data is used to discover the potentially predictive relationship. |
| C. | both a & b |
| D. | none of above |
| Answer» B. it is a set of data is used to discover the potentially predictive relationship. | |
| 283. |
What is Overfitting in Machine learning? |
| A. | when a statistical model describes random error or noise instead of underlying relationship overfitting occurs. |
| B. | robots are programed so that they can perform the task based on data they gather from sensors. |
| C. | while involving the process of learning overfitting occurs. |
| D. | a set of data is used to discover the potentially predictive relationship |
| Answer» B. robots are programed so that they can perform the task based on data they gather from sensors. | |
| 284. |
Which of the following sentence is correct? |
| A. | machine learning relates with the study, design and |
| B. | data mining can be defined as the process in which the |
| C. | both a & b |
| D. | none of the above |
| Answer» D. none of the above | |
| 285. |
During the last few years, many             algorithms have been applied to deep neural networks to learn the best policy for playing Atari video games and to teach an agent how to associate the right action with an input representing the state. |
| A. | logical |
| B. | classical |
| C. | classification |
| D. | none of above |
| Answer» E. | |
| 286. |
Which of the following are supervised learning applications |
| A. | spam detection, pattern detection, natural language processing |
| B. | image classification, real-time visual tracking |
| C. | autonomous car driving, logistic optimization |
| D. | bioinformatics, speech recognition |
| Answer» B. image classification, real-time visual tracking | |
| 287. |
if there is only a discrete number of possible outcomes (called categories), the process becomes a           . |
| A. | regression |
| B. | classification. |
| C. | modelfree |
| D. | categories |
| Answer» C. modelfree | |
| 288. |
Reinforcement learning is particularly efficient when                         . |
| A. | the environment is not completely deterministic |
| B. | it\s often very dynamic |
| C. | it\s impossible to have a precise error measure |
| D. | all above |
| Answer» E. | |
| 289. |
Commons unsupervised applications include |
| A. | object segmentation |
| B. | similarity detection |
| C. | automatic labeling |
| D. | all above |
| Answer» E. | |
| 290. |
what is the function of Supervised Learning? |
| A. | classifications, predict time series, annotate strings |
| B. | speech recognition, regression |
| C. | both a & b |
| D. | none of above |
| Answer» D. none of above | |
| 291. |
Common deep learning applications include |
| A. | image classification, real-time visual tracking |
| B. | autonomous car driving, logistic optimization |
| C. | bioinformatics, speech recognition |
| D. | all above |
| Answer» E. | |
| 292. |
What is Training set? |
| A. | training set is used to test the accuracy of the hypotheses generated by the learner. |
| B. | a set of data is used to discover the potentially predictive relationship. |
| C. | both a & b |
| D. | none of above |
| Answer» C. both a & b | |
| 293. |
What are the popular algorithms of Machine Learning? |
| A. | decision trees and neural networks (back propagation) |
| B. | probabilistic networks and nearest neighbor |
| C. | support vector machines |
| D. | all |
| Answer» E. | |
| 294. |
How can you avoid overfitting ? |
| A. | by using a lot of data |
| B. | by using inductive machine learning |
| C. | by using validation only |
| D. | none of above |
| Answer» B. by using inductive machine learning | |
| 295. |
According to        , its a key success factor for the survival and evolution of all species. |
| A. | claude shannon\s theory |
| B. | gini index |
| C. | darwins theory |
| D. | none of above |
| Answer» D. none of above | |
| 296. |
Suppose you are given ‘n’ predictions on test data by ‘n’ different models (M1, M2, …. Mn) respectively. Which of the following method(s) can be used to combine the predictions of these models? Note: We are working on a regression problem 1. Median 2. Product 3. Average 4. Weighted sum 5. Minimum and Maximum 6. Generalized mean rule |
| A. | 1, 3 and 4 |
| B. | 1,3 and 6 |
| C. | 1,3, 4 and 6 |
| D. | all of above |
| Answer» E. | |
| 297. |
How can we assign the weights to output of different models in an ensemble? 1. Use an algorithm to return the optimal weights 2. Choose the weights using cross validation 3. Give high weights to more accurate models |
| A. | 1 and 2 |
| B. | 1 and 3 |
| C. | 2 and 3 |
| D. | all of above |
| Answer» E. | |
| 298. |
Which of the following is true about averaging ensemble? |
| A. | it can only be used in classification problem |
| B. | it can only be used in regression problem |
| C. | it can be used in both classification as well as regression |
| D. | none of these |
| Answer» D. none of these | |
| 299. |
Which of the following is true about weighted majority votes? 1. We want to give higher weights to better performing models 2. Inferior models can overrule the best model if collective weighted votes for inferior models is higher than best model 3. Voting is special case of weighted voting |
| A. | 1 and 3 |
| B. | 2 and 3 |
| C. | 1 and 2 |
| D. | 1, 2 and 3 |
| Answer» E. | |
| 300. |
Which of the following are correct statement(s) about stacking? A machine learning model is trained on predictions of multiple machine learning models A Logistic regression will definitely work better in the second stage as compared to other classification methods 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. | all of above |
| Answer» D. all of above | |