Tutoria SVMs

Support vector machines (SVMs), are supervised learning models for classification and regression problems.

• True

• False

Answer explanation

SVM is a binary classification model.

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

The maximum distance between the data points of both classes is called:

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.

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.

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.

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

The SVM’s are less effective when:

• The data is linearly separable

• The data is clean and ready to use

• The data is noisy and contains overlapping points

Answer explanation

When the data has noise and overlapping points, there is a problem in drawing a clear hyperplane without misclassifying

What is/are true about kernel in SVM?

1. Kernel function map low dimensional data to high dimensional space

2. It’s a similarity function

• 1)

• 2)

• 1) and 2)

• None of these

Answer explanation

Both the given statements are correct.

The similarity function, called a kernel, is chosen so that it represents a dot product in some high-dimensional feature space.

The kernels allow the SVM to map all points to a high dimensional space that are more easily separated.

a) Kernel Function generally transforms the training set of data so that a non-linear decision surface is able to transform to a linear equation in a higher number of dimension spaces.

b) Solve the new optimisation problem (replace x, by f(x)).

Identify the statements are correct.

• a)

• b)

• a) and b)

• None