Linear SVM: A Case Study, First Part

( Coding And Theory)ANANDA SWARUP DAS, TECHNICAL STAFF MEMBER,

KNOWLEDGE ENGINEERING AND ENCRYPTED ANALYTICS TEAM,IBM INDIA RESEARCH LABS, NEW DELHI.

Spam and HamHow I read the data: Using Pandas

How My Data Looks:

Processing the Text

Number of Classes in My Data

Working with Text Data

TF-IDF (Term Frequency-Inverse Document Frequency Vectors)

Output

TfidfTranformer and Our Purpose

Objective:

TfidfTransformer:

Hyperplane Definition (Theory for Linear SVM Begins)

Role of the bias

The hyperplane that we want

A Criteria for Linear SVM to work

Linear SVM and the Decision Rules

Orthogonality of the Weight Vector

The Concept of Margin

Why Margin is Expressed in terms of the unit vector

Scaling the Margin