
Tutoria SVMs
Authored by LUCILINA VITÓRIA SPÍNOLA SOUSA
Mathematics
University
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10 questions
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1.
MULTIPLE CHOICE QUESTION
20 sec • 1 pt
Support vector machines (SVMs), are supervised learning models for classification and regression problems.
True
False
Answer explanation
SVM is a binary classification model.
2.
MULTIPLE CHOICE QUESTION
20 sec • 1 pt
1) The objective of the support vector machine algorithm is to find a hyperplane in an N-dimensional space (N — the number of features) that distinctly _________ the data points.
We use the optimization of _______ the margin (‘street width’) to ________ the number of weights that are nonzero to just a few that correspond to the important features that ‘matter’ in deciding the separating line(hyperplane).
classifies/ minimizing/ increase
classifies/ maximizing / reduce
estimates the value/ minimizing/ increase
estimates the value/ maximizing / reduce
Answer explanation
Maximising the margin distance provides some reinforcement so that future data points can be classified with more confidence.
In the SVM algorithm, we are looking to maximize the margin between the data points and the hyperplane
3.
FILL IN THE BLANK QUESTION
20 sec • 1 pt
The maximum distance between the data points of both classes is called:
4.
FILL IN THE BLANK QUESTION
20 sec • 1 pt
The decision boundary that help classify the data point, which is halfway between the two observations, is called:
Answer explanation
If the number of input features is 2, then the hyperplane is just a line.
If the number of input features is 3, then the hyperplane becomes a two-dimensional plane.
It is called an hyperplane when number of features exceeds 3.
5.
FILL IN THE BLANK QUESTION
20 sec • 1 pt
The Data points that are closer to the hyperplane and influence the position and orientation of the hyperplane are called: ______ vectors/ points.
Answer explanation
Support vectors are the elements of the training set that would change the position of the dividing hyperplane if removed. Nonzero weights.
Using these support points, we maximize the margin of the classifier. These are the points that help us build our SVM.
6.
MULTIPLE CHOICE QUESTION
20 sec • 1 pt
What sizes of datasets are not best suited for SVM’s?
Large datasets
Small datasets
It does not matter
Answer explanation
Datasets which have a clear classification boundary will function best with SVM’s.
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Support vectors are the data points that lie closest to the decision surface.
TRUE
FALSE
Answer explanation
They are the points closest to the hyperplane and the hardest ones to classify. They also have a direct bearing on the location of the decision surface
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