
Multiclass Classification Quiz
Authored by Michael Jimenez
Computers
Professional Development
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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is multiclass classification used for?
To identify outliers in the data
To predict the exact value of a continuous variable
To calculate probability values for a single class label
To predict to which of multiple possible classes an observation belongs
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the penguin species example, how is the species encoded?
0: Adelie, 1: Gentoo, 2: Chinstrap
Adelie, Gentoo, Chinstrap
A: Adelie, G: Gentoo, C: Chinstrap
1: Adelie, 2: Gentoo, 3: Chinstrap
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What are the two kinds of algorithms used to train a multiclass classification model?
Binary classification algorithms and Decision tree algorithms
Regression algorithms and Clustering algorithms
Support Vector Machine (SVM) algorithms and K-Nearest Neighbors (KNN) algorithms
One-vs-Rest (OvR) algorithms and Multinomial algorithms
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does a multinomial algorithm create?
A binary classification function for each class label
Multiple functions for each class label
A single function that returns a multi-valued output
A function that calculates the probability of the observation being a specific class
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the confusion matrix for a multiclass classifier different from that of a binary classifier?
It shows the number of predictions for each combination of predicted and actual class labels
It only shows the true positive and true negative values
It calculates the overall accuracy, recall, and precision metrics
It displays the probability distribution for all possible classes
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the overall accuracy of the multiclass classifier based on the given confusion matrix?
0.86
0.80
0.90
1.0
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the overall recall of the multiclass classifier based on the given confusion matrix?
0.86
0.90
0.80
1.0
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