McqMate
These multiplechoice questions (MCQs) are designed to enhance your knowledge and understanding in the following areas: Computer Science Engineering (CSE) .
301. 
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 doesn't belong to any of the above category. 
D.  both of these 
Answer» B. feature f1 is an example of ordinal variable. 
302. 
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» A. transform data to zero mean 
303. 
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» D. all above 
304. 
Can a model trained for item based similarity also choose from a given set of items? 
A.  yes 
B.  no 
Answer» A. yes 
305. 
What are common feature selection methods in regression task? 
A.  correlation coefficient 
B.  greedy algorithms 
C.  all above 
D.  none of these 
Answer» C. all above 
306. 
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» C. all above 
307. 
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» B. dataset 
308. 
adopts a dictionaryoriented approach, associating to each category label a progressive integer number. 
A.  labelencoder class 
B.  labelbinarizer class 
C.  dictvectorizer 
D.  featurehasher 
Answer» A. labelencoder class 
309. 
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.  couldn't comment on test error 
D.  test error is equal to train error 
Answer» C. couldn't comment on test error 
310. 
Which of the following metrics can be used for evaluating regression models?

A.  ii and iv 
B.  i and ii 
C.  ii, iii and iv 
D.  i, ii, iii and iv 
Answer» D. i, ii, iii and iv 
311. 
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» D. by its slope 
312. 
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» A. lm(formula, data) 
313. 
In syntax of linear model lm(formula,data,..), data refers to 
A.  matrix 
B.  vector 
C.  array 
D.  list 
Answer» B. vector 
314. 
In the mathematical Equation of Linear Regression Y = β1 + β2X + ϵ, (β1, β2) refers to 
A.  (xintercept, slope) 
B.  (slope, xintercept) 
C.  (yintercept, slope) 
D.  (slope, yintercept) 
Answer» C. (yintercept, slope) 
315. 
Linear Regression is a supervised machine learning algorithm. 
A.  true 
B.  false 
Answer» A. true 
316. 
It is possible to design a Linear regression algorithm using a neural network? 
A.  true 
B.  false 
Answer» A. true 
317. 
Overfitting is more likely when you have huge amount of data to train? 
A.  true 
B.  false 
Answer» B. false 
318. 
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.  can't say 
D.  none of these 
Answer» A. linear regression is sensitive to outliers 
319. 
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.  can't say 
D.  none of these 
Answer» A. since the there is a relationship means our model is not good 
320. 
Naive Bayes classifiers are a collection of algorithms 
A.  classification 
B.  clustering 
C.  regression 
D.  all 
Answer» A. classification 
321. 
Naive Bayes classifiers is Learning 
A.  supervised 
B.  unsupervised 
C.  both 
D.  none 
Answer» A. supervised 
322. 
Features being classified is independent of each other in Nave Bayes Classifier 
A.  false 
B.  true 
Answer» B. true 
323. 
Features being classified is of each other in Nave Bayes Classifier 
A.  independent 
B.  dependent 
C.  partial dependent 
D.  none 
Answer» A. independent 
324. 
Bayes Theorem is given by where 1. P(H) is the probability of hypothesis H being true.

A.  true 
B.  false 
Answer» A. true 
325. 
In given image, P(HE) is probability. 
A.  posterior 
B.  prior 
Answer» A. posterior 
326. 
In given image, P(H) is probability. 
A.  posterior 
B.  prior 
Answer» B. prior 
327. 
Conditional probability is a measure of the probability of an event given that another 
A.  true 
B.  false 
Answer» A. true 
328. 
Bayes theorem describes the probability of an event, based on prior knowledge of conditions that might be related to the event. 
A.  true 
B.  false 
Answer» A. true 
329. 
Bernoulli Nave Bayes Classifier is distribution 
A.  continuous 
B.  discrete 
C.  binary 
Answer» C. binary 
330. 
Multinomial Nave Bayes Classifier is distribution 
A.  continuous 
B.  discrete 
C.  binary 
Answer» B. discrete 
331. 
Gaussian Nave Bayes Classifier is distribution 
A.  continuous 
B.  discrete 
C.  binary 
Answer» A. continuous 
332. 
Binarize parameter in BernoulliNB scikit sets threshold for binarizing of sample features. 
A.  true 
B.  false 
Answer» A. true 
333. 
Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values. 
A.  mean 
B.  variance 
C.  discrete 
D.  random 
Answer» A. mean 
334. 
SVMs directly give us the posterior probabilities P(y = 1jx) and P(y = ??1jx) 
A.  true 
B.  false 
Answer» B. false 
335. 
Any linear combination of the components of a multivariate Gaussian is a univariate Gaussian. 
A.  true 
B.  false 
Answer» A. true 
336. 
Solving a non linear separation problem with a hard margin Kernelized SVM (Gaussian RBF Kernel) might lead to overfitting 
A.  true 
B.  false 
Answer» A. true 
337. 
SVM is a algorithm 
A.  classification 
B.  clustering 
C.  regression 
D.  all 
Answer» A. classification 
338. 
SVM is a learning 
A.  supervised 
B.  unsupervised 
C.  both 
D.  none 
Answer» A. supervised 
339. 
The linearSVMclassifier works by drawing a straight line between two classes 
A.  true 
B.  false 
Answer» A. true 
340. 
Which of the following function provides unsupervised prediction ? 
A.  cl_forecastb 
B.  cl_nowcastc 
C.  cl_precastd 
D.  none of the mentioned 
Answer» D. none of the mentioned 
341. 
Which of the following is characteristic of best machine learning method ? 
A.  fast 
B.  accuracy 
C.  scalable 
D.  all above 
Answer» D. all above 
342. 
What are the different Algorithm techniques in Machine Learning? 
A.  supervised learning and semisupervised learning 
B.  unsupervised learning and transduction 
C.  both a & b 
D.  none of the mentioned 
Answer» C. both a & b 
343. 
What is the standard approach to supervised learning? 
A.  split the set of example into the training set and the test 
B.  group the set of example into the training set and the test 
C.  a set of observed instances tries to induce a general rule 
D.  learns programs from data 
Answer» A. split the set of example into the training set and the test 
344. 
Which of the following is not Machine Learning? 
A.  artificial intelligence 
B.  rule based inference 
C.  both a & b 
D.  none of the mentioned 
Answer» B. rule based inference 
345. 
What is Model Selection in Machine Learning? 
A.  the process of selecting models among different mathematical models, which are used to describe the same data set 
B.  when a statistical model describes random error or noise instead of underlying relationship 
C.  find interesting directions in data and find novel observations/ database cleaning 
D.  all above 
Answer» A. the process of selecting models among different mathematical models, which are used to describe the same data set 
346. 
Which are two techniques of Machine Learning ? 
A.  genetic programming and inductive learning 
B.  speech recognition and regression 
C.  both a & b 
D.  none of the mentioned 
Answer» A. genetic programming and inductive learning 
347. 
Even if there are no actual supervisors learning is also based on feedback provided by the environment 
A.  supervised 
B.  reinforcement 
C.  unsupervised 
D.  none of the above 
Answer» B. reinforcement 
348. 
What does learning exactly mean? 
A.  robots are programed so that they can perform the task based on data they gather from sensors. 
B.  a set of data is used to discover the potentially predictive relationship. 
C.  learning is the ability to change according to external stimuli and remembering most of all previous experiences. 
D.  it is a set of data is used to discover the potentially predictive relationship. 
Answer» C. learning is the ability to change according to external stimuli and remembering most of all previous experiences. 
349. 
When it is necessary to allow the model to develop a generalization ability and avoid a common problem called . 
A.  overfitting 
B.  overlearning 
C.  classification 
D.  regression 
Answer» A. overfitting 
350. 
Techniques involve the usage of both labeled and unlabeled data is called . 
A.  supervised 
B.  semisupervised 
C.  unsupervised 
D.  none of the above 
Answer» B. semisupervised 
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