McqMate
401. 
Which of the following is true about Ridge or Lasso regression methods in case of feature selection? 
A.  ridge regression uses subset selection of features 
B.  lasso regression uses subset selection of features 
C.  both use subset selection of features 
D.  none of above 
Answer» B. lasso regression uses subset selection of features 
402. 
Which of the following statement(s) can 
A.  1 and 2 
B.  1 and 3 
C.  2 and 4 
D.  none of the above 
Answer» A. 1 and 2 
403. 
We can also compute the coefficient of linear regression with the help of an analytical method called Normal Equation.

A.  1 and 2 
B.  1 and 3. 
C.  2 and 3. 
D.  1,2 and 3. 
Answer» D. 1,2 and 3. 
404. 
If two variables are correlated, is it necessary that they have a linear relationship? 
A.  yes 
B.  no 
Answer» B. no 
405. 
Which of the following option is true regarding Regression and Correlation ?Note: y is dependent variable and x is independent variable. 
A.  the relationship is symmetric between x and y in both. 
B.  the relationship is not symmetric between x and y in both. 
C.  the relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric. 
D.  the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric. 
Answer» D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric. 
406. 
Suppose you are using a Linear SVM classifier with 2 class classification 
A.  yes 
B.  no 
Answer» A. yes 
407. 
If you remove the nonred circled points from the data, the decision boundary will change? 
A.  true 
B.  false 
Answer» B. false 
408. 
When the C parameter is set to infinite, which of the following holds true? 
A.  the optimal hyperplane if exists, will be the one that completely separates the data 
B.  the softmargin classifier will separate the data 
C.  none of the above 
Answer» A. the optimal hyperplane if exists, will be the one that completely separates the data 
409. 
Suppose you are building a SVM model on data X. The data X can be error prone which means that you should not trust any specific data point too much. Now think that you want to build a SVM model which has quadratic kernel function of polynomial degree 2 that uses Slack variable C as one of its hyper parameter.What would happen when you use very large value of C(C>infinity)? 
A.  we can still classify data correctly for given setting of hyper parameter c 
B.  we can not classify data correctly for given setting of hyper parameter c 
C.  can�t say 
D.  none of these 
Answer» A. we can still classify data correctly for given setting of hyper parameter c 
410. 
SVM can solvelinearand non linearproblems 
A.  true 
B.  false 
Answer» A. true 
411. 
The objective of the support vector machine algorithm is to find a hyperplane in an Ndimensional space(N the number of features) that distinctly classifies the data points. 
A.  true 
B.  false 
Answer» A. true 
412. 
Hyperplanes are boundaries that help classify the data points. 
A.  usual 
B.  decision 
C.  parallel 
Answer» B. decision 
413. 
The of the hyperplane depends upon the number of features. 
A.  dimension 
B.  classification 
C.  reduction 
Answer» A. dimension 
414. 
Hyperplanes are decision boundaries that help classify the data points. 
A.  true 
B.  false 
Answer» A. true 
415. 
SVMalgorithmsusea set of mathematical functions that are defined as thekernel. 
A.  true 
B.  false 
Answer» A. true 
416. 
In SVM, Kernel function is used to map a lower dimensional data into a higher dimensional data. 
A.  true 
B.  false 
Answer» A. true 
417. 
In SVR we try to fit the error within a 
A.  true 
B.  false 
Answer» A. true 
418. 
What is the purpose of performing cross validation? 
A.  to assess the predictive performance of the models 
B.  to judge how the trained model performs outside the sample on test data 
C.  both a and b 
Answer» C. both a and b 
419. 
Which of the following is true about Naive Bayes ? 
A.  assumes that all the features in a dataset are equally important 
B.  assumes that all the features in a dataset are independent 
C.  both a and b 
D.  none of the above option 
Answer» C. both a and b 
420. 
Which of the following isnotsupervised learning? 
A.  ��pca 
B.  ��decision tree 
C.  ��naive bayesian 
D.  linerar regression 
Answer» A. ��pca 
421. 
can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to impose some constraints to a clustering algorithm. 
A.  supervised 
B.  semisupervised 
C.  reinforcement 
D.  clusters 
Answer» B. semisupervised 
422. 
In reinforcement learning, this feedback is 
A.  overfitting 
B.  overlearning 
C.  reward 
D.  none of above 
Answer» C. reward 
423. 
In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called . 
A.  deep learning 
B.  machine learning 
C.  reinforcement learning 
D.  unsupervised learning 
Answer» A. deep learning 
424. 
there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an approach also allows simpler algorithms called 
A.  regression 
B.  accuracy 
C.  modelfree 
D.  scalable 
Answer» C. modelfree 
425. 
showed better performance than other approaches, even without a context based model 
A.  machine learning 
B.  deep learning 
C.  reinforcement learning 
D.  supervised learning 
Answer» B. deep learning 
426. 
If two variables are correlated, is it necessary that they have a linear relationship? 
A.  yes 
B.  no 
Answer» B. no 
427. 
Correlated variables can have zero correlation coeffficient. True or False? 
A.  true 
B.  false 
Answer» A. true 
428. 
Suppose we fit Lasso Regression to a data set, which has 100 features (X1,X2X100). Now, we rescale one of these feature by multiplying with 10 (say that feature is X1), and then refit Lasso regression with the same regularization parameter.Now, which of the following option will be correct? 
A.  it is more likely for x1 to be excluded from the model 
B.  it is more likely for x1 to be included in the model 
C.  can�t say 
D.  none of these 
Answer» B. it is more likely for x1 to be included in the model 
429. 
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 
430. 
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 
431. 
In syntax of linear model lm(formula,data,..), data refers to 
A.  matrix 
B.  vector 
C.  array 
D.  list 
Answer» B. vector 
432. 
Linear Regression is a supervised machine learning algorithm. 
A.  true 
B.  false 
Answer» A. true 
433. 
It is possible to design a Linear regression algorithm using a neural network? 
A.  true 
B.  false 
Answer» A. true 
434. 
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» A. least square error 
435. 
Suppose you are training a linear regression model. Now consider these points.1. Overfitting is more likely if we have less data2. Overfitting is more likely when the hypothesis space is small.Which of the above statement(s) are correct? 
A.  both are false 
B.  1 is false and 2 is true 
C.  1 is true and 2 is false 
D.  both are true 
Answer» C. 1 is true and 2 is false 
436. 
We can also compute the coefficient of linear regression with the help of an analytical method called Normal Equation. Which of the following is/are true about Normal Equation?1. We dont have to choose the learning rate2. It becomes slow when number of features is very large3. No need to iterate 
A.  1 and 2 
B.  1 and 3. 
C.  2 and 3. 
D.  1,2 and 3. 
Answer» D. 1,2 and 3. 
437. 
Which of the following option is true regarding Regression and Correlation ?Note: y is dependent variable and x is independent variable. 
A.  the relationship is symmetric between x and y in both. 
B.  the relationship is not symmetric between x and y in both. 
C.  the relationship is not symmetric between x and y in case of correlation but in case of regression it is symmetric. 
D.  the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric. 
Answer» D. the relationship is symmetric between x and y in case of correlation but in case of regression it is not symmetric. 
438. 
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 
439. 
Generally, which of the following method(s) is used for predicting continuous dependent variable?1. Linear Regression2. Logistic Regression 
A.  1 and 2 
B.  only 1 
C.  only 2 
D.  none of these. 
Answer» B. only 1 
440. 
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» B. 2 
441. 
In a real problem, you should check to see if the SVM is separable and then include slack variables if it is not separable. 
A.  true 
B.  false 
Answer» B. false 
442. 
Which of the following are real world applications of the SVM? 
A.  text and hypertext categorization 
B.  image classification 
C.  clustering of news articles 
D.  all of the above 
Answer» D. all of the above 
443. 
100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, was it a man? 
A.  true 
B.  false 
Answer» A. true 
444. 
For the given weather data, Calculate probability of playing 
A.  0.4 
B.  0.64 
C.  0.29 
D.  0.75 
Answer» B. 0.64 
445. 
In SVR we try to fit the error within a certain threshold. 
A.  true 
B.  false 
Answer» A. true 
446. 
In reinforcement learning, this feedback is usually called as . 
A.  overfitting 
B.  overlearning 
C.  reward 
D.  none of above 
Answer» C. reward 
447. 
Which of the following sentence is correct? 
A.  machine learning relates with the study, design and development of the algorithms that give computers the capability to learn without being explicitly programmed. 
B.  data mining can be defined as the process in which the unstructured data tries to extract knowledge or unknown interesting patterns. 
C.  both a & b 
D.  none of the above 
Answer» C. both a & b 
448. 
Reinforcement learning is particularly 
A.  the environment is not 
B.  it\s often very dynamic 
C.  it\s impossible to have a 
D.  all above 
Answer» D. all above 
449. 
Lets say, you are working with categorical feature(s) and you have not looked at the distribution of the categorical variable in the test data. You want to apply one hot encoding (OHE) on the categorical feature(s). What challenges you may face if you have applied OHE on a categorical variable of train dataset? 
A.  all categories of categorical variable are not present in the test dataset. 
B.  frequency distribution of categories is different in train as compared to the test dataset. 
C.  train and test always have same distribution. 
D.  both a and b 
Answer» D. both a and b 
450. 
Which of the following sentence is FALSE regarding regression? 
A.  it relates inputs to outputs. 
B.  it is used for prediction. 
C.  it may be used for interpretation. 
D.  it discovers causal relationships. 
Answer» D. it discovers causal relationships. 
451. 
Which of the following method is used to find the optimal features for cluster analysis 
A.  kmeans 
B.  densitybased spatial clustering 
C.  spectral clustering find clusters 
D.  all above 
Answer» D. all above 
452. 
scikitlearn also provides functions for creating dummy datasets from scratch: 
A.  make_classification() 
B.  make_regression() 
C.  make_blobs() 
D.  all above 
Answer» D. all above 
453. 
which can accept a NumPy RandomState generator or an integer seed. 
A.  make_blobs 
B.  random_state 
C.  test_size 
D.  training_size 
Answer» B. random_state 
454. 
In many classification problems, the target dataset is made up of categorical labels which cannot immediately be processed by any algorithm. An encoding is needed and scikitlearn offers at least valid options 
A.  1 
B.  2 
C.  3 
D.  4 
Answer» B. 2 
455. 
In which of the following each categorical label is first turned into a positive integer and then transformed into a vector where only one feature is 1 while all the others are 0. 
A.  labelencoder class 
B.  dictvectorizer 
C.  labelbinarizer class 
D.  featurehasher 
Answer» C. labelbinarizer class 
456. 
is the most drastic one and should be considered only when the dataset is quite large, the number of missing features is high, and any prediction could be risky. 
A.  removing the whole line 
B.  creating submodel to predict those features 
C.  using an automatic strategy to input them according to the other known values 
D.  all above 
Answer» A. removing the whole line 
457. 
It's possible to specify if the scaling process must include both mean and standard deviation using the parameters . 
A.  with_mean=true/false 
B.  with_std=true/false 
C.  both a & b 
D.  none of the mentioned 
Answer» C. both a & b 
458. 
Which of the following selects the best K highscore features. 
A.  selectpercentile 
B.  featurehasher 
C.  selectkbest 
D.  all above 
Answer» C. selectkbest 
459. 
How does number of observations influence overfitting? Choose the correct answer(s).Note: Rest all parameters are same1. In case of fewer observations, it is easy to overfit the data.2. In case of fewer observations, it is hard to overfit the data.3. In case of more observations, it is easy to overfit the data.4. In case of more observations, it is hard to overfit the data. 
A.  1 and 4 
B.  2 and 3 
C.  1 and 3 
D.  none of theses 
Answer» A. 1 and 4 
460. 
Suppose you have fitted a complex regression model on a dataset. Now, you are using Ridge regression with tuning parameter lambda to reduce its complexity. Choose the option(s) below which describes relationship of bias and variance with lambda. 
A.  in case of very large lambda; bias is low, variance is low 
B.  in case of very large lambda; bias is low, variance is high 
C.  in case of very large lambda; bias is high, variance is low 
D.  in case of very large lambda; bias is high, variance is high 
Answer» C. in case of very large lambda; bias is high, variance is low 
461. 
What is/are true about ridge regression?

A.  1 and 3 
B.  1 and 4 
C.  2 and 3 
D.  2 and 4 
Answer» A. 1 and 3 
462. 
Which of the following method(s) does not have closed form solution for its coefficients? 
A.  ridge regression 
B.  lasso 
C.  both ridge and lasso 
D.  none of both 
Answer» B. lasso 
463. 
Function used for linear regression in R 
A.  lm(formula, data) 
B.  lr(formula, data) 
C.  lrm(formula, data) 
D.  regression.linear(formula, 
Answer» A. lm(formula, data) 
464. 
In the mathematical Equation of Linear Regression Y?=??1 + ?2X + ?, (?1, ?2) refers to 
A.  (xintercept, slope) 
B.  (slope, xintercept) 
C.  (yintercept, slope) 
D.  (slope, yintercept) 
Answer» C. (yintercept, slope) 
465. 
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 its variable(Say X1) with Y is 0.95.Which of the following is true for X1? 
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» B. relation between the x1 and y is strong 
466. 
We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. Now we increase the training set size gradually. As the training set size increases, what do you expect will happen with the mean training error? 
A.  increase 
B.  decrease 
C.  remain constant 
D.  can't say 
Answer» D. can't say 
467. 
We have been given a dataset with n records in which we have input attribute as x and output attribute as y. Suppose we use a linear regression method to model this data. To test our linear regressor, we split the data in training set and test set randomly. What do you expect will happen with bias and variance as you increase the size of training data? 
A.  bias increases and variance increases 
B.  bias decreases and variance increases 
C.  bias decreases and variance decreases 
D.  bias increases and variance decreases 
Answer» D. bias increases and variance decreases 
468. 
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.  1 and 2 
B.  2 and 3 
C.  1 and 3 
D.  1, 2 and 3 
Answer» A. 1 and 2 
469. 
Problem:Players will play if weather is sunny. Is this statement is correct? 
A.  true 
B.  false 
Answer» A. true 
470. 
For the given weather data, Calculate probability of not playing 
A.  0.4 
B.  0.64 
C.  0.36 
D.  0.5 
Answer» C. 0.36 
471. 
Suppose you have trained an SVM with linear decision boundary after training SVM, you correctly infer that your SVM model is under fitting.Which of the following option would you more likely to consider iterating SVM next time? 
A.  you want to increase your data points 
B.  you want to decrease your data points 
C.  you will try to calculate more variables 
D.  you will try to reduce the features 
Answer» C. you will try to calculate more variables 
472. 
The minimum time complexity for training an SVM is O(n2). According to this fact, what sizes of datasets are not best suited for SVMs? 
A.  large datasets 
B.  small datasets 
C.  medium sized datasets 
D.  size does not matter 
Answer» A. large datasets 
473. 
What do you mean by generalization error in terms of the SVM? 
A.  how far the hyperplane is from the support vectors 
B.  how accurately the svm can predict outcomes for unseen data 
C.  the threshold amount of error in an svm 
Answer» B. how accurately the svm can predict outcomes for unseen data 
474. 
We usually use feature normalization before using the Gaussian kernel in SVM. What is true about feature normalization?

A.  1 
B.  1 and 2 
C.  1 and 3 
D.  2 and 3 
Answer» B. 1 and 2 
475. 
Support vectors are the data points that lie closest to the decision surface. 
A.  true 
B.  false 
Answer» A. true 
476. 
If I am using all features of my dataset and I achieve 100% accuracy on my training set, but ~70% on validation set, what should I look out for? 
A.  underfitting 
B.  nothing, the model is perfect 
C.  overfitting 
Answer» C. overfitting 
477. 
What is the purpose of performing cross validation? 
A.  to assess the predictive performance of the models 
B.  to judge how the trained model performs outside the 
C.  both a and b 
Answer» C. both a and b 
478. 
Suppose you are using a Linear SVM classifier with 2 class classification problem. Now you have been given the following data in which some points are circled red that are representing support vectors.If you remove the following any one red points from the data. Does the decision boundary will change? 
A.  yes 
B.  no 
Answer» A. yes 
479. 
Linear SVMs have no hyperparameters that need to be set by crossvalidation 
A.  true 
B.  false 
Answer» B. false 
480. 
For the given weather data, what is the probability that players will play if weather is sunny 
A.  0.5 
B.  0.26 
C.  0.73 
D.  0.6 
Answer» D. 0.6 
481. 
100 people are at party. Given data gives information about how many wear pink or not, and if a man or not. Imagine a pink wearing guest leaves, what is the probability of being a man 
A.  0.4 
B.  0.2 
C.  0.6 
D.  0.45 
Answer» B. 0.2 
482. 
Linear SVMs have no hyperparameters 
A.  true 
B.  false 
Answer» B. false 
483. 
What are the different Algorithm techniques in Machine Learning? 
A.  supervised learning and semi 
B.  unsupervised learning and transduction 
C.  both a & b 
D.  none of the mentioned 
Answer» C. both a & b 
484. 
can be adopted when it's necessary to categorize a large amount of data with a few complete examples or when there's the need to 
A.  supervised 
B.  semi supervised 
C.  reinforcement 
D.  clusters 
Answer» B. semi supervised 
485. 
In reinforcement learning, this feedback is usually called as . 
A.  overfitting 
B.  overlearning 
C.  reward 
D.  none of above 
Answer» C. reward 
486. 
In the last decade, many researchers started training bigger and bigger models, built with several different layers that's why this approach is called . 
A.  deep learning 
B.  machine learning 
C.  reinforcement learning 
D.  unsupervised learning 
Answer» A. deep learning 
487. 
What does learning exactly mean? 
A.  robots are programed so that they can 
B.  a set of data is used to discover the 
C.  learning is the ability to change 
D.  it is a set of data is used to discover the 
Answer» C. learning is the ability to change 
488. 
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 
489. 
Techniques involve the usage of both labeled and unlabeled data is called . 
A.  supervised 
B.  semi supervised 
C.  unsupervised 
D.  none of the above 
Answer» B. semi supervised 
490. 
there's a growing interest in pattern recognition and associative memories whose structure and functioning are similar to what happens in the neocortex. Such an 
A.  regression 
B.  accuracy 
C.  modelfree 
D.  scalable 
Answer» C. modelfree 
491. 
showed better performance than other approaches, even without a contextbased model 
A.  machine learning 
B.  deep learning 
C.  reinforcement learning 
D.  supervised learning 
Answer» B. deep learning 
492. 
Which of the following sentence is correct? 
A.  machine learning relates with the study, 
B.  data mining can be defined as the process 
C.  both a & b 
D.  none of the above 
Answer» C. both a & b 
493. 
What is ‘Overfitting’ in Machine learning? 
A.  when a statistical model describes random error or noise instead of 
B.  robots are programed so that they can perform the task based on data they gather from 
C.  while involving the process of learning ‘overfitting’ occurs. 
D.  a set of data is used to discover the potentially predictive relationship 
Answer» A. when a statistical model describes random error or noise instead of 
494. 
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» A. test set is used to test the accuracy of the hypotheses generated by the learner. 
495. 
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» C. both a & b 
496. 
Commons unsupervised applications include 
A.  object segmentation 
B.  similarity detection 
C.  automatic labeling 
D.  all above 
Answer» D. all above 
497. 
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» D. all above 
498. 
During the last few years, many algorithms have been applied to deep

A.  logical 
B.  classical 
C.  classification 
D.  none of above 
Answer» D. none of above 
499. 
Common deep learning applications include 
A.  image classification, realtime visual tracking 
B.  autonomous car driving, logistic optimization 
C.  bioinformatics, speech recognition 
D.  all above 
Answer» D. all above 
500. 
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» B. classification. 
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