🧪 Machine Learning (ML) MCQ Quiz Hub

1. Application of machine learning methods to large databases is called
data mining.
artificial intelligence
big data computing
internet of things

2. If machine learning model output involves target variable then that model is called as
descriptive model
predictive model
reinforcement learning
All of the above

3. In what type of learning labelled training data is used
unsupervised learning
supervised learning
reinforcement learning
active learning

4. In following type of feature selection method we start with empty feature set
forward feature selection
both a and b??
backword feature selection
None of the above

5. Which of the following is the best machine learning method?
scalable
accuracy
fast
All of the above

6. What characterize unlabeled examples in machine learning
there is no prior knowledge
there is no confusing knowledge
there is prior knowledge
there is plenty of confusing knowledge

7. What does dimensionality reduction reduce?
stochastics
collinerity
performance
entropy

8. Data used to build a data mining model.
training data
validation data
test data
hidden data

9. The problem of finding hidden structure in unlabeled data is called…
supervised learning
unsupervised learning
reinforcement learning
None of the above

10. Of the Following Examples, Which would you address using an supervised learning Algorithm?
given email labeled as spam or not spam, learn a spam filter
given a set of news articles found on the web, group them into set of articles about the same story.
given a database of customer data, automatically discover market segments and group customers into different market segments.
find the patterns in market basket analysis

11. You are given reviews of few netflix series marked as positive, negative and neutral. Classifying reviews of a new netflix series is an example of
supervised learning
unsupervised learning
semisupervised learning
reinforcement learning

12. Which of the following is a good test dataset characteristic?
large enough to yield meaningful results
is representative of the dataset as a whole
both a and b
none of the above

13. Following are the types of supervised learning
classification
regression
subgroup discovery
all of the above

14. Type of matrix decomposition model is
descriptive model
logical model
logical model
none of the above

15. ollowing is powerful distance metrics used by Geometric model
euclidean distance
manhattan distance
both a and b??
square distance

16. The output of training process in machine learning is
machine learning model
machine learning algorithm
null
accuracy

17. A feature F1 can take certain value: A, B, C, D, E, & F and represents grade of students from a college. Here feature type is
nominal
ordinal
categorical
boolean

18. PCA is
forward feature selection
backword feature selection
feature extraction
all of the above

19. Which of the following techniques would perform better for reducing dimensions of a data set?
removing columns which have too many missing values
removing columns which have high variance in data
removing columns with dissimilar data trends
None of these

20. Supervised learning and unsupervised clustering both require which is correct according to the statement.
output attribute
hidden attribute.
input attribute
categorical attribute

21. What characterize is hyperplance in geometrical model of machine learning?
a plane with 1 dimensional fewer than number of input attributes
a plane with 2 dimensional fewer than number of input attributes
a plane with 1 dimensional more than number of input attributes
a plane with 2 dimensional more than number of input attributes

22. Like the probabilistic view, the ________ view allows us to associate a probability of membership with each classification.
exampler
deductive
classical
inductive

23. Database query is used to uncover this type of knowledge.
deep
hidden
shallow
multidimensional

24. A person trained to interact with a human expert in order to capture their knowledge.
knowledge programmer
knowledge developer r
knowledge engineer
knowledge extractor

25. Some telecommunication company wants to segment their customers into distinct groups ,this is an example of
supervised learning
reinforcement learning
unsupervised learning
data extraction

26. In the example of predicting number of babies based on stork's population ,Number of babies is
outcome
feature
observation
attribute

27. Which learning Requires Self Assessment to identify patterns within data?
unsupervised learning
supervised learning
semisupervised learning
reinforced learning

28. Select the correct answers for following statements. 1. Filter methods are much faster compared to wrapper methods. 2. Wrapper methods use statistical methods for evaluation of a subset of features while Filter methods use cross validation.
both are true
1 is true and 2 is false
both are false
1 is false and 2 is true

29. The "curse of dimensionality" referes
all the problems that arise when working with data in the higher dimensions, that did not exist in the lower dimensions.
all the problems that arise when working with data in the lower dimensions, that did not exist in the higher dimensions.
all the problems that arise when working with data in the lower dimensions, that did not exist in the lower dimensions.
all the problems that arise when working with data in the higher dimensions, that did not exist in the higher dimensions.

30. In simple term, machine learning is
training based on historical data
prediction to answer a query
both a and b??
automization of complex tasks

31. If machine learning model output doesnot involves target variable then that model is called as
descriptive model
predictive model
reinforcement learning
all of the above

32. Following are the descriptive models
clustering
classification
association rule
both a and c

33. Different learning methods does not include?
memorization
analogy
deduction
introduction

34. A measurable property or parameter of the data-set is
training data
feature
test data
validation data

35. Feature can be used as a
binary split
predictor
both a and b??
None of the above

36. The most popularly used dimensionality reduction algorithm is Principal Component Analysis (PCA). Which of the following is/are true about PCA? 1. PCA is an unsupervised method2. It searches for the directions that data have the largest variance3. Maximum number of principal components <= number of features4. All principal components are orthogonal to each other
1 & 2
2 & 3
3 & 4
all of the above

37. Which of the following is a reasonable way to select the number of principal components "k"?
choose k to be the smallest value so that at least 99% of the varinace is retained.
choose k to be 99% of m (k = 0.99*m, rounded to the nearest integer).
choose k to be the largest value so that 99% of the variance is retained.
use the elbow method

38. Which of the folllowing is an example of feature extraction?
construction bag of words from an email
applying pca to project high dimensional data
removing stop words
forward selection

39. Prediction is
the result of application of specific theory or rule in a specific case
discipline in statistics used to find projections in multidimensional data
value entered in database by expert
independent of data

40. You are given sesimic data and you want to predict next earthquake , this is an example of
supervised learning
reinforcement learning
unsupervised learning
dimensionality reduction

41. PCA works better if there is 1. A linear structure in the data 2. If the data lies on a curved surface and not on a flat surface 3. If variables are scaled in the same uni
1 and 2
2 and 3
1 and 3
1,2 and 3

42. A student Grade is a variable F1 which takes a value from A,B,C and D. Which of the following is True in the following case?
variable f1 is an example of nominal variable
variable f1 is an example of ordinal variable
it doesn belong to any of the mentioned categories
it belongs to both ordinal and nominal category

43. What can be major issue in Leave-One-Out-Cross-Validation(LOOCV)?
low variance
high variance
faster runtime compared to k-fold cross validation
slower runtime compared to normal validation

44. Imagine a Newly-Born starts to learn walking. It will try to find a suitable policy to learn walking after repeated falling and getting up.specify what type of machine learning is best suited?
classification
regression
kmeans algorithm
reinforcement learning

45. Support Vector Machine is
logical model B.
proababilistic model
geometric model
none of the above

46. In multiclass classification number of classes must be
less than two
equals to two
greater than two
option 1 and option 2

47. Which of the following can only be used when training data are linearlyseparable?
linear hard-margin svm
linear logistic regression
linear soft margin svm
the centroid method

48. Impact of high variance on the training set ?
overfitting
underfitting
both underfitting & overfitting
depents upon the dataset

49. What do you mean by a hard margin?
the svm allows very low error in classification
the svm allows high amount of error in classification
both 1 & 2
none of the above

50. The effectiveness of an SVM depends upon:
selection of kernel
kernel parameters
soft margin parameter c
all of the above