Presentation_Rashmi

 SLIDE 1

What is SVM?

“Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. However,  it is mostly used in classification problems.

SVM_1

SLIDE 2

how does it work ?

Identify the right hyper-plane (Scenario-1):

SVM_2
SLIDE3:

Identify the right hyper-plane (Scenario-2): 


SVM_3

SLIDE4:

Here, maximizing the distances between nearest data point (either class) and hyper-plane will help us to decide the right hyper-plane. This distance is called as Margin

SVM_4

SLIDE5:

Identify the right hyper-plane (Scenario-3):


SVM_5

SLIDE6:

Find the hyper-plane to segregate to classes 


SVM_8

SVM_9


SLIDE 

Cost function and Gradient Function

In the SVM algorithm, we are looking to maximize the margin between the data points and the hyperplane. The loss function that helps maximize the margin is hinge loss.

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SLIDE 

Regularization

The objective of the regularization parameter is to balance the margin maximization and loss.

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SLIDE 


When there is a misclassification, i.e our model make a mistake on the prediction of the class of our data point, we include the loss along with the regularization parameter to perform gradient update

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