Difference between Supervised and Unsupervised Learning

Navigating the realm of machine learning, many grapple with understanding the key disparities between supervised and unsupervised learning. This article aims to elucidate these differences, addressing questions on input data, computational complexities, real-time analysis, and the reliability of results.

Supervised learning

When an algorithm is trained on a labelled dataset—that is, when the input data used for training is paired with corresponding output labels—it is referred to as supervised learning. Supervised learning aims to find a mapping or relationship between the input variables and the desired output, which enables the algorithm to produce precise predictions or classifications when faced with fresh, unobserved data.

An input-output pair training set is given to the algorithm during a supervised learning process. For every example in the training set, the algorithm iteratively modifies its parameters to minimize the discrepancy between its predicted output and the actual output (the ground truth). This procedure keeps going until the algorithm performs at an acceptable level.

Supervised learning can be divided into two main types:

1. Regression: In regression problems, the goal is to predict a continuous output or value. For example, predicting the price of a house based on its features, such as the number of bedrooms, square footage, and location.
2. Classification: In classification problems, the goal is to assign input data to one of several predefined categories or classes. Examples include spam email detection, image classification (e.g., identifying whether an image contains a cat or a dog), and sentiment analysis.

Why supervised learning?

The basic aim is to approximate the mapping function(mentioned above) so well that when there is a new input data (x) then the corresponding output variable can be predicted. It is called supervised learning because the process of learning(from the training dataset) can be thought of as a teacher who is supervising the entire learning process. Thus, the “learning algorithm” iteratively makes predictions on the training data and is corrected by the “teacher”, and the learning stops when the algorithm achieves an acceptable level of performance (or the desired accuracy). 

Supervised Learning Example

Suppose there is a basket which is filled with some fresh fruits, the task is to arrange the same type of fruits in one place. Also, suppose that the fruits are apple, banana, cherry, and grape. Suppose one already knows from their previous work (or experience) that, the shape of every fruit present in the basket so, it is easy for them to arrange the same type of fruits in one place. Here, the previous work is called training data in Data Mining terminology. So, it learns things from the training data. This is because it has a response variable that says y that if some fruit has so and so features then it is grape, and similarly for every fruit. This type of information is deciphered from the data that is used to train the model. This type of learning is called Supervised Learning. Such problems are listed under classical Classification Tasks.  

Unsupervised Learning

Unsupervised learning is a type of machine learning where the algorithm is given input data without explicit instructions on what to do with it. In unsupervised learning, the algorithm tries to find patterns, structures, or relationships in the data without the guidance of labelled output.

The main goal of unsupervised learning is often to explore the inherent structure within a set of data points. This can involve identifying clusters of similar data points, detecting outliers, reducing the dimensionality of the data, or discovering patterns and associations.

There are several common types of unsupervised learning techniques:

1. Clustering: Clustering algorithms aim to group similar data points into clusters based on some similarity metric. K-means clustering and hierarchical clustering are examples of unsupervised clustering techniques.
2. Dimensionality Reduction: These techniques aim to reduce the number of features (or dimensions) in the data while preserving its essential information. Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE) are examples of dimensionality reduction methods.
3. Association: Association rule learning is used to discover interesting relationships or associations between variables in large datasets. The Apriori algorithm is a well-known example used for association rule learning.

Why Unsupervised Learning?

The main aim of Unsupervised learning is to model the distribution of the data to learn more about the data. It is called unsupervised learning because there is no correct answer and there is no such teacher(unlike supervised learning). Algorithms are left to their own devices to discover and present an interesting structure in the data. 

Unsupervised Learning example

Again, Suppose there is a basket and it is filled with some fresh fruits. The task is to arrange the same type of fruits in one place. This time there is no information about those fruits beforehand, it’s the first time that the fruits are being seen or discovered So how to group similar fruits without any prior knowledge about them? First, any physical characteristic of a particular fruit is selected. Suppose colour. Then the fruits are arranged based on the color. 
The groups will be something as shown below: 

• RED COLOR GROUP: apples & cherry fruits. 
• GREEN COLOR GROUP: bananas & grapes. So now, take another physical character say, size, so now the groups will be something like this. 
• RED COLOR AND BIG SIZE: apple. 
• RED COLOR AND SMALL SIZE: cherry fruits. 
• GREEN COLOR AND BIG SIZE: bananas. 
• GREEN COLOR AND SMALL SIZE: grapes.

The job is done! Here, there is no need to know or learn anything beforehand. That means, no train data and no response variable. This type of learning is known as Unsupervised Learning. 

Difference between Supervised and Unsupervised Learning

The distinction between supervised and unsupervised learning depends on whether the learning algorithm uses pattern-class information. Supervised learning assumes the availability of a teacher or supervisor who classifies the training examples, whereas unsupervised learning must identify the pattern-class information as a part of the learning process.

Supervised learning algorithms utilize the information on the class membership of each training instance. This information allows supervised learning algorithms to detect pattern misclassifications as feedback to themselves. In unsupervised learning algorithms, unlabeled instances are used. They blindly or heuristically process them. Unsupervised learning algorithms often have less computational complexity and less accuracy than supervised learning algorithms. 

	Supervised Learning	Unsupervised Learning
Input Data	Uses Known and Labeled Data as input	Uses Unknown Data as input
Computational Complexity	Less Computational Complexity	More Computational Complex
Real-Time	Uses off-line analysis	Uses Real-Time Analysis of Data
Number of Classes	The number of Classes is known	The number of Classes is not known
Accuracy of Results	Accurate and Reliable Results	Moderate Accurate and Reliable Results
Output data	The desired output is given.	The desired, output is not given.
Model	In supervised learning it is not possible to learn larger and more complex models than in unsupervised learning	In unsupervised learning it is possible to learn larger and more complex models than in supervised learning
Training data	In supervised learning training data is used to infer model	In unsupervised learning training data is not used.
Another name	Supervised learning is also called classification.	Unsupervised learning is also called clustering.
Test of model	We can test our model.	We can not test our model.
Example	Optical Character Recognition	Find a face in an image.

Conclusion

In conclusion, the article unravels the intricate tapestry of supervised and unsupervised learning, shedding light on their roles in data analysis. Whether classifying known data or exploring uncharted territories, these methodologies play crucial roles in shaping the landscape of artificial intelligence.