Ever googled machine learning? Well, if you have, you would be aware that types of machine learning appear on the top of your search. And one of the topics that interests most readers is supervised vs unsupervised learning. While, understanding the basics of supervised vs unsupervised learning is not something very difficult for the tech experts, it might be a good idea to break down the concepts for the consumption of others as well.
Supervised vs Unsupervised Learning: The Meaning
To not complicate things too much, we have a simple way to demystify the terms. Supervised learning is the type of machine learning which takes place under supervision. On the other hand, unsupervised learning is when machine learning takes place in the absence of any supervision or guidance.
Supervised vs Unsupervised Learning: Nature of Input Data
Depending on whether supervision is a need or not, the nature of input data differs across supervised vs unsupervised learning.
The nature of input data for supervised learning is ‘labelled’. This simply translates to the fact that some data already contains the tags with the correct answers.
On the other hand, in unsupervised learning, the data is primarily unlabelled. Developers using this form of machine learning have to allow the machine to discover all answers on its own.
Supervised vs Unsupervised Learning: Categories
As is true with most concepts of technology, the two types of machine learning further have specific groups and categories
Supervised learning consists of two major types: Regression & Classification. The difference between the two lies in the nature of their output variable. Regression is when the output variable is in the form of a real value. For instance, size, weight, height, economic value, etc. Classification, on the other hand, is when the output variable is in the form of a class or a category. Choosing between ‘pink’ or ‘white’ or ‘tall’ or ‘short’ fall under the category of classification. Labelling of input data into exactly two categories is binary classification. Labelling into more than two classes is multiclass classification.
Similarly, unsupervised learning can be grouped into Clustering & Association. Developers apply clustering when they seek to identify patterns or structure within a group of uncategorized data. Association, on the other hand, comes in place when the intent is to identify associations between different data objects amidst heavy databases. For instance, people who have a baby, will baby proof their homes.
Supervised vs Unsupervised Learning: Objective
As the nature of input data and output variable differs, it goes without saying that the objective of supervised vs unsupervised learning will also be different.
The objective of supervised learning is to leverage previous experiences to produce or collect data. The most common purpose for which developers consider it to be the perfect match is to undertake predictive analysis.
Unsupervised learning comes with a different objective altogether. It focuses on identifying unknown patterns and structures. The objective is simple, to identify features that can come in handy for categorization.
Supervised vs Unsupervised Learning: Application
Depending on what they offer and seek to achieve, supervised vs unsupervised learning have varied applications.
Supervised learning has its applications across several sectors. For instance, marketing and sales adopt it to identify customer lifetime value, churn rate and even sentiment analysis. It also finds application in the domain of market forecasting. Supervised learning has multiple applications for security as well, including spam filtration and fraud detection. Undoubtedly, there are several other ways in which its application is taking place.
Unsupervised learning, likewise, has its unique application. Targeted marketing and customer marketing are two applied areas where unsupervised learning is yielding great results. Visual recognition is another upcoming and promising area of application.
To put it in a nutshell, while the larger objective of supervised vs unsupervised machine learning converges, there are clear distinctions that appear. What they seek to achieve, their application and even the input are quite different for both these types of machine learning. If you have any machine learning need, please reach out to us and engage with our machine learning developers seamlessly!