
Support Vector Machines and k-NN Quiz
Authored by Saranya P
Computers
University
Used 4+ times

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15 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary objective of Support Vector Machines (SVM)?
To maximize the margin between classes
To minimize the number of support vectors
To reduce training time
To maximize the number of misclassified points
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In SVM, what is a Support Vector?
A data point closest to the decision boundary
A point farthest from the hyperplane
A randomly chosen data point
A data point with the highest weight
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which kernel function is commonly used in SVM for handling non-linearly separable data?
Linear Kernel
Polynomial Kernel
Radial Basis Function (RBF) Kernel
Sigmoid Kernel
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
k-Nearest Neighbors (k-NN) is a type of:
Supervised Learning Algorithm
Unsupervised Learning Algorithm
Reinforcement Learning Algorithm
Semi-Supervised Learning Algorithm
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How does k-NN classify a new data point?
By computing the mean of all data points
By finding the k closest training examples and taking a majority vote
By using a decision tree to find the class
By selecting a random class
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In SVM, what is the purpose of the kernel trick?
To increase the number of dimensions
To transform data into higher-dimensional space for better separation
To make SVM faster
To remove noise from the dataset
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What happens when a very small value of 'k' is used in k-NN?
The model generalizes well
The model becomes less sensitive to noise
The model may overfit the training data
The model underfits the data
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