Difference Between Supervised and Unsupervised Learning

By K Balaji|Updated : November 29th, 2022

The major difference between supervised and unsupervised learning is that supervised learning will be used when we know the input and its corresponding output, and unsupervised learning will be used when we know the input but do not know the output. Supervised and unsupervised learning is the machine learning approach.

Difference Between Supervised and Unsupervised Learning PDF

The major difference between supervised and unsupervised learning is that supervised learning is a function that maps an input to an output based on example input-output pairs. The unsupervised learning approach of machine learning seeks previously unnoticed patterns in a data set with no pre-existing labels and minimal human supervision. Let us check the difference between supervised and unsupervised learning.

Difference Between Supervised and Unsupervised Learning

The two machine learning strategies are supervised learning and unsupervised learning. However, each technique has a different application and uses a different dataset. There are several difference between supervised and unsupervised learning. Some of these differences are listed below.

Supervised vs Unsupervised Learning

Difference Between Supervised and Unsupervised Learning

Supervised Learning

Unsupervised Learning

We teach the machine using the labeled data in supervised learning.

In unsupervised learning, we teach the machine using unlabelled data.

In supervised learning, there is external supervision.

In unsupervised learning, there is no external supervision.

Supervised learning aims to predict the output.

Unsupervised learning aims to discover the unknown pattern and discover the output.

Supervised learning uses a training dataset.

Unsupervised learning uses an input dataset.

Supervised learning is used for prediction.

Unsupervised learning is used for analysis.

The algorithm learns from the association between the input and output.

The algorithm derives the structure from the input by looking at the relation between the input.

Supervised learning can use offline analysis.

Unsupervised learning uses the real-time analysis of data.

Supervised and Unsupervised Learning

The model must be trained through supervision in supervised learning. In unsupervised learning, the model can be trained without any supervision. Both supervised and unsupervised learning methods have pros and cons. The differences between supervised and unsupervised learning are provided below.

What is Supervised Learning?

Supervised learning is the approach of machine learning. Supervised learning is defined as it uses labelled datasets, and these datasets are made to “supervise” or train the algorithms to predict the output.

The use of labelled datasets distinguishes the machine learning strategy known as supervised learning. These datasets are intended to "supervise" or "train" algorithms to correctly classify data or forecast outcomes. Labelled inputs and outputs allow the model to monitor its precision and improve over time.

There are two primary types of supervised learning methods:

  • Classification: Classification issues, such as distinguishing apples from oranges, employ an algorithm to accurately assign test data into specific categories.
  • Regression: Another supervised learning method is regression, which employs an algorithm to deduce the relationship between dependent and independent variables.

What is Unsupervised Learning?

Unsupervised learning is another approach to machine learning. Unsupervised learning is used to cluster and analyze unlabeled datasets. These algorithms implement hidden patterns in the data without the need for human intervention.

Machine learning algorithms are used in unsupervised learning to examine and group unlabeled data sets. These algorithms are referred to as "unsupervised" since they identify hidden patterns in data without the assistance of a person.

There are three types of unsupervised learning methods:

  • Clustering: A data mining technique for sorting unlabelled data into groups based on similarities and differences.
  • Association: Another unsupervised learning method is an association, which employs several rules to discover associations between variables in a dataset.
  • Dimensionality Reduction: When the number of features (or dimensions) in a dataset is too great, dimensionality reduction is a learning technique that is applied.

Why should you go for Supervised Learning?

The primary goal is to estimate the mapping function (described above) well enough that the related output variable can be predicted when new input data (x) is introduced. Because learning (from the training dataset) can be thought of as an instructor or a teacher directing the whole learning process, it is called supervised learning. As a result, the "learning algorithm" generates predictions on the training data iteratively and is corrected by the "teacher." Learning ends when the algorithm achieves an acceptable level of performance (or the desired accuracy).

Why should you go for Unsupervised Learning?

Unsupervised learning's primary goal is to model the data's distribution to learn more about it. It is so named because there is no proper answer and no such instructor or teacher (unlike supervised learning). Algorithms are left on their own to discover and present the data's intriguing structure.

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FAQs on Difference Between Supervised and Unsupervised Learning

  • The difference between supervised and unsupervised learning is that unsupervised learning uses the unlabelled data for input and output of the code, whereas supervised learning uses labelled data for input and output of the code.

  • The difference between supervised and unsupervised learning regarding data mining is that unsupervised learning in data mining is the descriptive technique. In contrast, supervised learning in data mining is the predictive technique

  • The real-time analysis of data takes place in the case of unsupervised learning. Of the two methods, supervised learning is known to provide more accurate and reliable results. Although, the computation of results is quite complex and challenging to understand in the case of supervised learning. 

  • The Supervised Learning algorithms that are most frequently employed are decision trees, logistic regression, linear regression, and support vector machines. Examples of Unsupervised Learning algorithms are K-means clustering, hierarchical clustering, and apriori algorithms.

  • Supervised learning is time series because a sequence of numbers is given from the times series dataset. We can reconstruct that data so that the data looks the same as supervised learning of machine learning.

  • Supervised learning is a decision tree because the decision tree will be explained by two entities which are decision leaves and nodes. The decision tree is a type of supervised learning where the data is split continuously according to a certain parameter.

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