The world is getting “smarter” every day at the speed of light. Companies are constantly making efforts to use machine learning algorithms to make things easier to fulfill consumer expectations. Some day-to-day examples include end-user devices (through face recognition for unlocking smartphones) or detecting credit card fraud (like triggering alerts for unusual purchases).
What makes this world smart and more technology-driven are machine learning (ML) and artificial intelligence (AI). Machine learning is dependent on two types of algorithms – supervised learning and unsupervised learning.
One significant difference between the two approaches is one works under the surveillance of labeled data while the other does not require any of it. However, some discrepancies between the two approaches in some particular areas make them different from each other.
Supervised learning can be defined as a machine learning approach defined by its use of labeled datasets. These datasets are used to design trained or “supervise” algorithms to classify data or predict outcomes accurately. The models can measure their accuracy and learn over time through labeled inputs and outputs.
For example, consider learning the model where the input variable, say X, and the output variable, say Y, is mapped into an algorithm to generate the required results.
Inference, Y = f(X).
Data mining is performed in supervised learning in two different processes – classification and regression.
The classification method is applied to the algorithms that accurately assign test data into specific categories like separating numbers from alphabets. In a real-world example, supervised learning is used to classify spam emails separately from the inbox. The common types of classification algorithms include linear classifiers, support vector machines, decision trees, and random forests.
In the regression method, supervised learning uses an algorithm that describes the relationship between dependent and independent variables. The regression method proves helpful in predicting numerical values that are based on a different data point, such as determining sales statistics projections for a given business.
Some popular regression algorithms include linear regression, logistic regression, and polynomial regression.
The following example will help to understand supervised learning –
Consider a basket that is full of different kinds of fresh fruit such as apple, bananas, cherries, grapes. The target here is to arrange similar types of fruits in different baskets based on their individuality.
If the machine has already worked on a similar activity, it becomes easy to fulfill the tasks. Based on its previous activity, the machine has already gained the knowledge to perform the task, such as it already knows the shape of each fruit present in the basket; it can easily segregate and arrange the same type of fruits in one basket.
Here, machine learning extracts knowledge from previous work obtained in the form of training data in Data Mining terminology.
In supervised learning, only the input data is present with no corresponding output variable. At the same time, unsupervised learning uses the hidden or underlying structure for the distribution of the data to learn more details.
Unsupervised learning makes use of machine learning algorithms to analyze and cluster unlabeled data sets. These algorithms work on identifying hidden patterns in data without any need for human intervention.
Another name for unsupervised learning is knowledge discovery.
Unsupervised learning is used to perform three main tasks – clustering, association, and dimensionality reduction.
Clustering, a data mining technique, is used to group unlabeled data based on their similarities or differences. Clustering algorithms process raw and unclassified data objects into groups that represent structures or patterns in the information.
Clustering algorithms are classified into different types, such as specifically exclusive, overlapping, hierarchical, and probabilistic.
Association is another type of unsupervised learning that uses different rules to find a relationship between variables in a given dataset. Such methods are constantly used for market basket analysis and recommendation engines, on the outline of “Customers Who Bought This Item Also Bought” recommendations.
- Dimensionality reduction
The dimensionality reduction method is used when the number of features or dimensions in a given dataset is very high. It aims to help in reducing the number of data inputs to a limited size while maintaining the data integrity of the datasets as much as possible.
Such a method is often used in the pre-processing data stage; for example, autoencoders may use it to remove noise from visual data to improve picture quality.
At times, more amount of data usually yields more accurate results. It also has a massive impact on the performance of machine learning algorithms (e.g., overfitting) while It can also make it challenging to visualize datasets.
The following example will help to understand unsupervised learning better-
Again, consider a basket that is full of different kinds of fresh fruit such as apples, bananas, cherries, and grapes. The target here is to arrange the similar type of fruits in different baskets based on their individuality.
In this case, the machine does not have any previous knowledge of fruits. This will be the first time the machine encounters such new objects. This is how the machine then processes the tasks –
- Selects any physical characteristic of a particular fruit
- Arranges fruits based on their color
- Red color: apples and cherry
- Green color: bananas and grapes
- Now along with color, it will learn about the size too
- Red color and big size: apple
- Red color and small size: cherry
- Green color and big size: bananas
- Green color and small size: grapes
This is how the task gets completed.
Here, no prior information is required, meaning there is no need for any training data.
It isn’t easy to choose between both the learnings. The answers to both learnings depend on the situation and the task given to it. This blog provides you brief information on what is supervised and unsupervised learning. Come back to this space in a while to learn more about their difference as well as what could be a midway solution for them.
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