Machine Learning (ML) MCQ Quiz Hub

1. Which of the following evaluation metrics can be used to evaluate a model while modeling a continuous output variable?
auc-roc
accuracy
logloss
mean-squared-error

2. Which of the following is true about Residuals ?
lower is better
higher is better
a or b depend on the situation
none of these

3. Which of the following statement is true about outliers in Linear regression?
linear regression is sensitive to outliers
linear regression is not sensitive to outliers
cant say
None of These

4. 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?
since the there is a relationship means our model is not good
since the there is a relationship means our model is good
cant say
none of these

5. Naive Bayes classifiers are a collection------------------of algorithms
classification
clustering
regression
All of the above

6. Naive Bayes classifiers is Learning
supervised
unsupervised
both
none

7. Features being classified is of each other in Nave Bayes Classifier
independent
dependent
partial dependent
None of These

8. Multinomial Nave Bayes Classifier is distribution
continuous
discrete
binary
None of these

9. Gaussian Nave Bayes Classifier is distribution
continuous
discrete
binary
None of These

10. Gaussian distribution when plotted, gives a bell shaped curve which is symmetric about the of the feature values.
mean
variance
discrete
random

11. SVM is a algorithm
classification
clustering
regression
All of the above

12. SVM is a learning
supervised
unsupervised
both
None of These

13. Which of the following function provides unsupervised prediction ?
cl_forecastb
cl_nowcastc
cl_precastd
None of the mentioned

14. Which of the following is characteristic of best machine learning method ?
fast
accuracy
scalable
all above

15. What are the different Algorithm techniques in Machine Learning?
supervised learning and semi-supervised learning
unsupervised learning and transduction
both a & b
None of the mentioned

16. What is the standard approach to supervised learning?
split the set of example into the training set and the test
group the set of example into the training set and the test
a set of observed instances tries to induce a general rule
learns programs from data

17. Which of the following is not Machine Learning?
artificial intelligence
rule based inference
both a & b
None of the mentioned

18. What is Model Selection in Machine Learning?
the process of selecting models among different mathematical models, which are used to describe the same data set
when a statistical model describes random error or noise instead of underlying relationship
find interesting directions in data and find novel observations/ database cleaning
all above

19. Which are two techniques of Machine Learning ?
genetic programming and inductive learning
speech recognition and regression
both a & b
none of the mentioned

20. Even if there are no actual supervisors learning is also based on feedback provided by the environment
supervised
reinforcement
unsupervised
None of the above

21. What does learning exactly mean?
robots are programed so that they can perform the task based on data they gather from sensors.
a set of data is used to discover the potentially predictive relationship.
learning is the ability to change according to external stimuli and remembering most of all previous experiences.
it is a set of data is used to discover the potentially predictive relationship.

22. When it is necessary to allow the model to develop a generalization ability and avoid a common problem called .
overfitting
overlearning
classification
regression

23. Techniques involve the usage of both labeled and unlabeled data is called .
supervised
semi-supervised
unsupervised
none of the above

24. In reinforcement learning if feedback is negative one it is defined as .
penalty
overlearning
reward
None of the above

25. According to , its a key success factor for the survival and evolution of all species.
claude shannons theory
gini index
darwins theory
none of above

26. A supervised scenario is characterized by the concept of a .
programmer
teacher
author
farmer

27. overlearning causes due to an excessive .
capacity
regression
reinforcement
accuracy

28. Which of the following is an example of a deterministic algorithm?
pca
k-means
both (a) and (b)
None of the above

29. Which of the following model model include a backwards elimination feature selection routine?
mcv
mars
mcrs
All of the above

30. Which of the following are several models
regression
classification
both (a) and (b)
None of the above

31. provides some built-in datasets that can be used for testing purposes.
scikit-learn
classification
regression
None of the above

32. While using all labels are turned into sequential numbers.
labelencoder class
labelbinarizer class
dictvectorizer
featurehasher

33. produce sparse matrices of real numbers that can be fed into any machine learning model.
dictvectorizer
featurehasher
both a & b
None of the mentioned

34. scikit-learn offers the class , which is responsible for filling the holes using a strategy based on the mean, median, or frequency
labelencoder
labelbinarizer
dictvectorizer
imputer

35. Which of the following scale data by removing elements that don't belong to a given range or by considering a maximum absolute value.
minmaxscaler
maxabsscaler
both a & b
None of the mentioned

36. scikit-learn also provides a class for per- sample normalization,
normalizer
imputer
classifier
All of the above

37. dataset with many features contains information proportional to the independence of all features and their variance.
normalized
unnormalized
both a & b
None of the mentioned

38. In order to assess how much information is brought by each component, and the correlation among them, a useful tool is the .
concuttent matrix
convergance matrix
supportive matrix
covariance matrix

39. The parameter can assume different values which determine how the data matrix is initially processed
run
start
init
stop

40. allows exploiting the natural sparsity of data while extracting principal components.
sparsepca
kernelpca
svd
init parameter

41. Which of the following is true about Residuals ?
lower is better
higher is better
a or b depend on the situation
none of these

42. Which of the following statement is true about outliers in Linear regression?
linear regression is sensitive to outliers
linear regression is not sensitive to outliers
cant say
none of these

43. 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?
since the there is a relationship means our model is not good
since the there is a relationship means our model is good
cant say
None of These

44. Lets say, a Linear regression model perfectly fits the training data (train error is zero). Now, Which of the following statement is true?
you will always have test error zero
you can not have test error zero
Both 1 and 2
none of the above

45. In a linear regression problem, we are using R-squared to measure goodness-of-fit. We add a feature in linear regression model and retrain the same model. Which of the following option is true?
if r squared increases, this variable is significant.
if r squared decreases, this variable is not significant.
individually r squared cannot tell about variable importance. we cant say anything about it right now.
none of these

46. Which of the one is true about Heteroskedasticity?
linear regression with varying error terms
linear regression with constant error terms
linear regression with zero error terms D.
none of these

47. Which of the following assumptions do we make while deriving linear regression parameters?1. The true relationship between dependent y and predictor x is linear2. The model errors are statistically independent3. The errors are normally distributed with a 0 mean and constant standard deviation4. The predictor x is non-stochastic and is measured error-free
1,2 and 3.
1,3 and 4
1 and 3.
All of the above

48. To test linear relationship of y(dependent) and x(independent) continuous variables, which of the following plot best suited?
scatter plot
barchart
histograms
none of these

49. which of the following step / assumption in regression modeling impacts the trade- off between under-fitting and over-fitting the most.
the polynomial degree
whether we learn the weights by matrix inversion or gradient descent
the use of a constant-term
None of These

50. Which of the following is true about Ridge or Lasso regression methods in case of feature selection?
ridge regression uses subset selection of features
lasso regression uses subset selection of features
both use subset selection of features
None of the above

51. Which of the following statement(s) can be true post adding a variable in a linear regression model?1. R-Squared and Adjusted R-squared both increase2. R- Squared increases and Adjusted R-
1 and 2
1 and 3
2 and 4
None of the above

52. How many coefficients do you need to estimate in a simple linear regression model (One independent variable)?
1
2
cant say
None of These

53. What is/are true about kernel in SVM?1. Kernel function map low dimensional data to high dimensional space2. Its a similarity function
1
2
1 and 2
None of these

54. 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 small C (C~0)?
misclassification would happen
data will be correctly classified
cant say
none of these

55. How do you handle missing or corrupted data in a dataset?
a. drop missing rows or columns
replace missing values with mean/median/mode
assign a unique category to missing values
all of the above

56. The SVMs are less effective when:
the data is linearly separable
the data is clean and ready to use
the data is noisy and contains overlapping points
None of These

57. If there is only a discrete number of possible outcomes called
modelfree
categories
prediction
none of above

58. Some people are using the term instead of prediction only to avoid the weird idea that machine learning is a sort of modern magic.
inference
interference
accuracy
None of the above

59. The term can be freely used, but with the same meaning adopted in physics or system theory.
accuracy
cluster
regression
prediction

60. Common deep learning applications / problems can also be solved using
real-time visual object identification
classic approaches
automatic labeling
bio-inspired adaptive systems