Machine Learning - Classification MCQs
Machine Learning - Classification - MCQs
1.
By default,
SGD classifier follows this strategy for multi-class classification:
A) OvO strategy
B) OvA strategy
C) Both
D) None
Ans: B
2. SGD Classifiers and Linear Classifiers are strictly
A) Binary Classifier
B) Multiclass classifier
C) Both
D) None
Ans: A
3.
The greater
the value for ROC AUC, better the model:
A) True
B) False
Ans: A
4.
The maximum
value of the ROC AUC is
A) 0.8
B) 0.9
C) 1
D) 0.7
Ans: C
5.
Recall can
be increased by increasing the decision threshold. True or False?
A) False
B)
True
Ans: A
6.
Precision
can be increased by increasing the decision threshold. True or False?
A) True
B) False
Ans: A
7.
Which of
these is a good measure to decide which threshold to use?
A) Confusion matrix
B) F1 score
C) ROC curve
D) Precision & Recall versus Threshold Curve
Ans: D
8. SVM Classifier scales poorly with the size of
training dataset. For SVM, which strategy for multi-class classifier should be
applied?
A) OvA
B) OvO
Ans: B
9.
Is
RandomForestClassifier a multinomial classifier?
A)
Yes
B)
No
Ans: A
10.
For
RandomForestClassifier, do we need to run either OvA or OvO classifier at all?
A)
Yes
B)
No
Ans: B
11.
Which of
these classifiers support multilabel classification?
A) SGD Classifier
B) SVM Classifier
C) KNeighbours Classifier
D) None
Ans: C
12. For MNIST dataset multiclass classification, SGD
Classifiers trains how many binary classifiers with OvA strategy?
A) 8
B) 10
C) 12
D) 45
Ans: B
13. For MNIST dataset multiclass classification, SGD
Classifiers train how many classifiers using OvO strategy?
A) 8
B) 10
C) 12
D) 45
Ans: D
14.
Classifying
MNIST dataset image into [large or small, odd or even] classification lables is
an example of:
A) Binary Classification
B) Multiclass Classification
C) Multilabel Classification
D) Multioutput Classification
Ans: C
15.
In
multilabel classification, which of the following 'average' method for
calculating F1 score calculates the unweighted mean of the f1 score of
individual labels?
A) macro
B) binary
C) micro
D) weighted
Ans: A
16. In multilabel classification, which of the
following average method for calculating F1 score calculates the metrics for
each label, and find their average weighted by support (the number of true
instances for each label)?
A) Macro
B) Binary
C) Micro
D) Weighted
Ans: D
17. Which of the following methods would not be a good
measure for skewed datasets. For example, 5 and Not 5 classiifer in MNIST has a
skewed dataset in which there are more 'Not 5's as compared to '5's?
A) cross_val_score using accuracy
B) confusion matrix
C) Cross_val_score using precision
D) None
Ans: A
18. Multiclass classifiers are also known as:
A) Mutlilabel classifiers
B) Multinomial classifiers
C) Multioutput classifiers
D) None
Ans: B
19. MNIST - 5 and Not 5 problem is what kind of a
problem?
A) Classification
B) Regression
C) Clustering
D) None
Ans: A
20. MNIST - 5 and Not 5 Classification is what kind of
a classification problem?
A) Binary Classification
B) Multi-class
C) Multi-label
D) Multi-output
Ans: A
21.
Why do we
use random_state in Stochastic Gradient Descent classifier?
A) For generating reproducible results
B) To specify the training size of the batch for each
iteration
C) Both
D) None
Ans: A
22. Which of these may have to be performed before
analyzing and training the dataset?
A) Shuffling
B) Cross-Validation
C) F1 Score
D) All of the above
Ans: A
23. For the below confusion matrix, what is the total
number of training datasets?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 50000
B) 60000
C) 70000
D) 80000
Ans: B
24. For the below confusion matrix, what is the count
of True Positive?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 53272
B) 1077
C) 1307
D) 4344
Ans: D
25. For the below confusion matrix, what is the count
of True Negatives?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 53272
B) 1077
C) 1307
D) 4344
Ans: A
26. For the below confusion matrix, what is the count
of False Negatives?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 53272
B) 1077
C) 1307
D) 4344
Ans: B
27. For the below confusion matrix, what is the count
of False Positive?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 53272
B) 1077
C) 1307
D) 4344
Ans: C
28. For the below confusion matrix, what is the
accuracy?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 95%
B) 90%
C) 96%
D) 98%
Ans: C
29. For the below confusion matrix, what is the recall?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 0.7
B) 0.8
C) 0.9
D) 0.95
Ans: B
30. For the below confusion matrix, what is the
precision?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A)
0.73
B)
0.76
C)
0.78
D)
0.82
Ans: B
31. F1 score is:
A) absolute mean of precision and recall
B) harmonic mean of precision and recall
C) squared mean of precision and recall
D) All of the above
Ans: B
32. For the below confusion matrix, what is the F1
score?
|
Not 5 |
5 |
|
|
Not 5 |
53272 |
1307 |
|
5 |
1077 |
4344 |
A) 0.72
B) 0.784
C) 0.82
D) 0.84
Ans: B
33. For a model to detect videos that are unsafe for
kids, we need (safe video = postive class)
A) High precision, low recall
B) High recall, low precision
C) High Precision, High Recall
D) Low Precision, Low Recall
Ans: A
34. For a model to detect shoplifters in surveillance
images, we need (shoplifter is postive class)
A) High precision, low recall
B) High recall, low precision
C) High Precision, High Recall
D) Low Precision, Low Recall
Ans: B
35. Which of the following can be treated as a
multi-output classification problem?
A) Removing noise from MNIST image
B) Classifying MNIST dataset into 0 to 9
C) Predicted the demand for rental bikes
D) None
Ans: A
36.
37.
38.
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