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Quiz Topic - Classification
1.
Which of the following metrics are used to evaluate classification models?
A. Area under the ROC curve
B. F1 score
C. Confusion matrix
D. All of the above
view answer:
D. All of the above
2.
Which one is a classification algorithm?
A. Logistic regression
B. Linear regression
C. Polynomial regression
D. None
view answer:
A. Logistic regression
3.
Classification is-
A. Unsupervised learning
B. Reinforcement learning
C. Supervised learning
D. None
view answer:
C. Supervised learning
4.
You have a dataset of different flowers containing their petal lengths and color. Your model has to predict the type of flower for given petal lengths and color. This is a-
A. Regression task
B. Classification task
C. Clustering task
D. None
view answer:
B. Classification task
5.
A classifier-
A. Inputs a vector of continuous values and outputs a single discrete value
B. Inputs a vector of discrete values and outputs a single discrete value
C. Both A and B
D. None
view answer:
C. Both A and B
6.
Classification is appropriate when you-
A. Try to predict a continuous valued output
B. Try to predict a class or discrete output
C. Both A and B for different contexts
D. None
view answer:
B. Try to predict a class or discrete output
7.
With the help of a confusion matrix, we can compute-
A. Recall
B. Precision
C. Accuracy
D. All of the above
view answer:
D. All of the above
8.
What does recall refer to in classification?
A. The proportion of all the relevant data points
B. The proportion of only the correct data points
C. The proportion of all data points
D. None
view answer:
A. The proportion of all the relevant data points
9.
False negatives are-
A. Predicted negatives that are actually positives
B. Predicted positives that are actually negatives
C. Predicted negatives that are actually negatives
D. Predicted positives that are actually positives
view answer:
A. Predicted negatives that are actually positives
10.
Suppose your classification model predicted true for a class which actual value was false. Then this is a-
A. False positive
B. False negative
C. True positive
D. True negative
view answer:
A. False positive
11.
The false negative value is 5 and the true positive value is 20. What will be the value of recall-
A. 0.2
B. 0.6
C. 0.8
D. 0.3
view answer:
C. 0.8
12.
The true positive value is 10 and the false positive value is 15. Calculate the value of precision-
A. 0.6
B. 0.4
C. 0.5
D. None
view answer:
B. 0.4
13.
If the precision is 0.6 and the recall value is 0.4. What will be the f measure?
A. 0.5
B. 0.6
C. 0.4
D. 0.3
view answer:
A. 0.5
14.
Which one is a different algorithm?
A. Logistic Regression
B. Support Vector Regression
C. Polynomial Regression
D. None
view answer:
A. Logistic Regression
15.
What is a support vector?
A. The distance between any two data points
B. The average distance between all the data points
C. The distance between two boundary data points
D. The minimum distance between any two data points
view answer:
C. The distance between two boundary data points
16.
Which of the following is a lazy learning algorithm?
A. SVM
B. KNN
C. Decision tree
D. All of the above
view answer:
B. KNN
17.
Which of the following is not a lazy learning algorithm?
A. SVM
B. Decision tree
C. Random forest
D. All of the above
view answer:
D. All of the above
18.
What is the most widely used distance metric in KNN?
A. Euclidean distance
B. Manhattan distance
C. Perpendicular distance
D. All of the above
view answer:
A. Euclidean distance
19.
Which of the following is the best algorithm for text classification?
A. KNN
B. Decision tree
C. Random forest
D. Naive Bayes
view answer:
D. Naive Bayes
20.
What does k stand for in the KNN algorithm?
A. Number of neighbors
B. Number of output classes
C. Number of input features
D. None
view answer:
A. Number of neighbors
21.
Support Vector Machine is-
A. a discriminative classifier
B. a lazy learning classifier
C. a probabilistic classifier
D. None
view answer:
A. a discriminative classifier
22.
What are hyperplanes?
A. Decision boundaries
B. Decision functions
C. Mapping functions
D. None
view answer:
A. Decision boundaries
23.
What is a kernel?
A. A function that calculates the distance of two boundary data points
B. A function that maps the value from one dimension to the other
C. A function that predicts the output value of a regression
D. None
view answer:
B. A function that maps the value from one dimension to the other
24.
Which of the following is not a kernel?
A. Polynomial Kernel
B. Gaussian Kernel
C. Sigmoid Kernel
D. None
view answer:
D. None
25.
Why Naive Bayes is called naive?
A. Because its assumption may or may not true
B. Because itâ€™s a bad classifier
C. The accuracy is very poor
D. All of the above
view answer:
A. Because its assumption may or may not true
26.
For two events A and B, the Bayes theorem will be-
A. P(A | B) = P(B) * P(B | A) / P(A)
B. P(A | B) = P(A) * P(B | A) / P(B)
C. P(A | B) = P(B) * P(A | B) / P(A)
D. P(A | B) = P(A) * P(A | B) / P(B)
view answer:
B. P(A | B) = P(A) * P(B | A) / P(B)
27.
How does a decision tree work?
A. Minimizes the information gain and maximizes the entropy
B. Maximizes the information gain and minimizes the entropy
C. Minimizes the information gain and minimizes the entropy
D. Maximizes the information gain and maximizes the entropy
view answer:
B. Maximizes the information gain and minimizes the entropy
28.
Suppose you have a dataset that is randomly distributed. What will be the best algorithm for that dataset?
A. Support vector machine
B. Naive Bayes
C. K nearest neighbors
D. Decision tree
view answer:
D. Decision tree
29.
Which pair of the algorithms are similar in operation?
A. SVM and KNN
B. Decision tree and Random forest
C. SVM and Naive Bayes
D. All of the above
view answer:
B. Decision tree and Random forest
30.
Which metric is not used for evaluating classification models?
A. AUC ROC score
B. Accuracy
C. Mean absolute error
D. Precision
view answer:
C. Mean absolute error
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