Classifying data into predefined categories

Input and output for classification problem

• Input to classification problem is a feature and output is called as label
• Problem statement and training data is where we spend amount of time

Lets talk about 2 types of problems

  Problem statement 1

     Email, tweet or trading day

• Types of problems are Spam or Ham
• Tweet positive or negative
• Trading day up-day or down-day

     Problem statement 2

•  Build the black box classifier . What happens in this black box is represented using mathematical rules or equations and it is called a model

     Features

• Every data point that we see needs to be represented as numerical attributes
• The algorithms only except numerical algorithms
• Even text and images are represented using numeric

     Training

• We take large amount of historical data.These are set of problem instances that are correctly labelled. Ex emails that are marked as 
• Each part of training data is tuples of features and label
• The patterns the classifier learns in this phase is classified as Model 

           Note: All does not follow this

• If the output that is requires is known, Then it is known as supervised learning

      Test Phase

• Here we are actually classify the data that we have not seen before

Note: Most of these the algorithms are available as pre-built libraries for platforms like Python,R or spark

Algorithms for classification problem

• Naive Bayes
• Support Vector Machines
• Decision Trees
• K-Nearest Neighbors
• Random Forest
• Logistic Regression

Term Frequency Representation

 This is how we take the text input and represent it as set of numeric data