Difference between supervised and unsupervised learning.

Computer Programming Database Differences

Businesses around the world today are smart and do everything to get and retain their customers. They can identify malicious credit/debit card transactions, they can identify a person uniquely with face or eye detection as a password to unlock a device, offer what their customer are looking for in the least possible time, separate spams from regular emails, and predict within how much time one can reach their intended destination depending upon length of road, weather conditions, and traffic, etc.

These challenging tasks are possible only when the algorithms carrying out such predictions are smart, and the learning approaches are the ones which make the algorithms smart.

When it comes to data mining, there are two main approaches of Machine Learning −

Supervised learning
Unsupervised learning

Read through this article to find out more about supervised and unsupervised learning and how they are different from each other.

What is Supervised Learning Approach?

The supervised learning approach of machine learning is the approach with which the algorithms are trained by using labelled datasets. The datasets train the algorithms to be smarter. They make it easy for the algorithms to predict the outcome as accurate as possible.

A dataset is the collection of related yet discrete data, which can be used or managed individually as well as a group. The labelled datasets are the named pieces of data that are tagged with one or multiple labels pertaining to certain properties or characteristics.

For example, look at the picture below. It depicts classification and labelling −

The labelled datasets make the algorithms understand the relationship among the datasets and carry out classification or prediction as a new outcome quickly with utmost accuracy. In this approach, human intervention is necessary to define properties and characteristics as well as to label the data appropriately.

Supervised Learning is used for the data where the input and output data can be precisely mapped.

Different Approaches of Supervised Learning

Supervised learning is divided further into two approaches −

Classification − In this approach, algorithms are trained to categorize the data into distinct units depending on their labels. Examples of some classification algorithms are − Decision Tree, Random Forest, Support Vector Machine, etc. Classification can be of types Binary and Multi-class.
Regression − This approach makes a computer program understand the relationship between dependent and independent data. As the name suggests regression means "going back to", the algorithm is exposed to the past data. Once training the algorithm is completed, the algorithm can predict the future values easily. Some popular regression algorithms are Linear, Logistic, and Polynomial regression. Regression can be of types Linear and Non-linear.

Both the above algorithms of machine learning are used for prediction. Both the algorithms work with the labelled datasets. Then what is the difference between the two?

Difference between Classification and Regression Algorithms

The prominent difference between Classification and Regression algorithms is that the Regression algorithms are used to predict continuous values such as height, weight, cost, salary, weather, etc. In contrast, the Classification algorithms are used to classify or predict discrete values such as True or False, Valid or Invalid, Yes or No, Spam or Not Spam, Male or Female, etc.

What is Unsupervised Learning Approach?

The unsupervised learning approach of machine learning does not use labelled datasets for training the algorithms. Instead, the machines learn on their own by accessing massive amount of unclassified data and finding its implicit patterns. The algorithms analyze and cluster the unlabelled datasets. There is no human intervention required while analyzing and clustering hence the name "Unsupervised".

Different Approaches of Unsupervised Learning

The unsupervised learning approach is of the following three types −

Association −This approach uses some rules to find relationships between variables in a dataset. This approach is often used in suggestions and recommendation. For example, suggesting an item to a customer with: "The customers who bought this item also bought", or "You may also like", or simply by showing allied product images and recommending to buy related items. For example, when the primary product being purchased is a computer, then suggesting to buy a wireless mouse and a remote keyboard too.
Clustering − It is a learning technique in data mining where unlabelled or unclassified data are grouped depending on either similarities or differences among them. This technique is helpful for the businesses to understand market segments depending on the customers demographics.
Dimensionality Reduction − It is a learning technique used to reduce the number of random variables or ‘dimensions’ to obtain a set of principal variables, when the number of variables is very high. This technique helps data compression without compromising the usability of the data. This learning is used for pre-processing of the audio/visual data to improve the quality of the outcome or making the background of an image transparent.

Why is Unsupervised Learning Essential?

Unsupervised learning is essential because of the following reasons −

Unlabelled, uncategorized data is available in abundance.
Unsupervised learning can explore unknown patterns of data.
Labelling the data is a tedious task, which also can allow human errors in Supervised learning, which is not the case with Unsupervised learning.

Difference between Supervised and Unsupervised Learning

The following table highlights the major differences between Supervised and Unsupervised learning −

Factor	Supervised Learning	Unsupervised Learning
Objective	To train the algorithm for prediction. The outcome the algorithm predicts mostly occurs as per the human expectation.	To train the algorithm to find insights from the large volume of unclassified data.
Dataset Labelling	The datasets used in Supervised learning are labelled.	The data used in Unsupervised learning are unclassified.
Knowledge of Classes	The classes of data are known.	The number of classes is unknown as the model data is uncategorized and unlabelled.
Human Intervention	In supervised learning, human intervention is required to label the data appropriately.	The unsupervised learning makes the algorithm to take care of both; the input and the output of the data analizing but human intervention is only required for data validation.
Proximity with Artificial Intelligence	With remarkable amount of human intervention, Supervised learning seems distant from the real Artificial Intelligence.	With the less amount of human intervention, Unsupervised learning is very close to Artificial Intelligence.
Computational Complexity	It is simple and inexpensive.	It is complicated, timeconsuming, and requires more resources.
Learning Process	In Supervised learning, the process of training the algorithm takes place offline.	In case of unsupervised learning, the process of training the algorithms takes place in real time.
Accuracy of the Outcome	It provides highly accurate outcome. The accuracy can be hampered only if the experts who are labelling the datasets didn’t label them appropriately.	Unsupervised learning is less accurate.

Conclusion

Machine Learning approaches can be either Supervised or Unsupervised. If you can anticipate the expanse of data, and if it is possible to divide the data into categories, then the best approach is to help the algorithm become smarter by Supervised Learning.

If you anticipate that the amount of data is massive, and if you think that the data cannot be simply classified or labelled, then it is better to go for Unsupervised Learning approach and let the algorithms handle predictions smartly.