en_Intro to Classification.txt

Hello!
In this video, we’ll give you an introduction to Classification.
So let’s get started.
In Machine Learning, classification is a supervised learning approach, which can be thought of
as a means of categorizing or "classifying" some unknown items into a discrete set of
"classes."
Classification attempts to learn the relationship between a set of feature variables and a target
variable of interest.
The target attribute in classification is a categorical variable with discrete values.
So, how does classification and classifiers work?
Given a set of training data points, along with the target labels, classification determines
the class label for an unlabeled test case.
Let’s explain this with an example.
A good sample of classification is the loan default prediction.
Suppose a bank is concerned about the potential for loans not to be repaid.
If previous loan default data can be used to predict which customers are likely to have
problems repaying loans, these "bad risk" customers can either have their loan application
declined or offered alternative products.
The goal of a loan default predictor is to use existing loan default data, which is information
about the customers (such as age, income, education, etc.), to build a classifier, pass
a new customer or potential future defaulter to the model, and then label it (i.e. the
data points) as "Defaulter" or "Not Defaulter", or for example, 0 or 1.
This is how a classifier predicts an unlabeled test case.
Please notice that this specific example was about a binary classifier with two values.
We can also build classifier models for both binary classification and multi-class classification.
For example, imagine that you collected data about a set of patients, all of whom suffered
from the same illness.
During their course of treatment, each patient responded to one of three medications.
You can use this labeled dataset, with a classification algorithm, to build a classification model.
Then you can use it to find out which drug might be appropriate for a future patient
with the same illness.
As you can see, it is a sample of multi-class classification.
Classification has different business use cases as well, for example:
To predict the category to which a customer belongs;
For Churn detection, where we predict whether a customer switches to another provider or
brand; Or to predict whether or not a customer responds
to a particular advertising campaign.
Data classification has several applications in a wide variety of industries.
Essentially, many problems can be expressed as associations between feature and target
variables, especially when labeled data is available.
This provides a broad range of applicability for classification.
For example, classification can be used for email filtering, speech recognition, handwriting
recognition, bio-metric identification, document classification, and much more.
Here we have the types of classification algorithms in machine learning.
They include: Decision Trees, Naïve Bayes, Linear Discriminant Analysis, K-nearest neighbor,
Logistic regression, Neural Networks, and Support Vector Machines.
There are many types of classification algorithms.
We will only cover a few in this course.
Thanks for watching.