What is Classification ? How does it work ?
According to Wikipedia:
In machine learning and statistics, classification is the problem of identifying to which of a set of categories (sub-populations) a new observation belongs, on the basis of a training set of data containing observations (or instances) whose category membership is known. Examples are assigning a given email to the “spam” or “non-spam” class, and assigning a diagnosis to a given patient based on observed characteristics of the patient (gender, blood pressure, presence or absence of certain symptoms, etc.).
In Simple language:
Suppose we have a multi-class/category population/dataset. Now let us say we have a new data/point, and we want to determine the class to which my new data belongs.
Note:- The number of classes in classification is countable.
Mathematically Lets say we have a function f(x) which determines the classes to which any given data belongs. The function f(x) is generated by training with a large data set.
And in the similar fashion classification works.
There are basically two stages of classification:
1. Training stage:- As mentioned above, in training stage we generally provide data set to an algorithm which in turn generates a function f(x).
2. Test/Evaluation Stage:- In the second stage the generated function gets ready to get into action. The function is used to determine the class/category to which any given random data belongs.
I’ll write more blogs on continuation of this post where i will cover every lengths and breadths of each concept very easy and vivid manner. So keep visiting and share the blog. Thanks for reading.
Originally published at www.siliconbuddy.com on July 22, 2018.
