Supervised vs Unsupervised Learning: Key Differences

Last updated by Nitin Grover on Aug 24, 2024 at 12:27 PM | Reading time: 7 minutes

Machine learning is a field of study that enables computers to learn by themselves and without being explicitly programmed to do so. In today’s world machine learning has a lot of applications. It's used everywhere, from shopping and video recommendations on sites like Amazon and Youtube respectively, to facial recognition in computers and mobile phones and giving query response capabilities to Alexa and Siri.

Machine learning can be classified into supervised and unsupervised learning. This article provides information on these two machine learning types. It further identifies the differences between the two and provides a few examples.

Supervised vs Unsupervised Learning: Definition and Classification

Let’s first understand the meaning of supervised vs unsupervised learning.

Supervised Machine Learning

Machine learning algorithms are first trained on a labeled dataset in supervised learning. Based on that dataset, they are required to predict the output.

The supervised algorithm knows the correct output for each item in a trained data set. The algorithm uses this knowledge to make accurate predictions on new, unseen data. By the use of labeled inputs and outputs, the models can measure their accuracy and learn over time.

In supervised learning, the algorithm learns from training datasets by iteratively making predictions on the data and then adjusting for the correct answer.‍

Example: Supervised learning can compute how your upcoming commute to the office will be as its raining along the way. However, first you need to train the model with data related to how rainy weather extends driving time.

Unsupervised Machine Learning

In unsupervised learning, the machine learning models work on their own to discover the inherent structure of unlabeled data. These models don’t need human intervention or any data set to be trained with. They can automatically find patterns in data and group them.

Example: Unsupervised learning models can cluster images by the objects they contain, such as people, monuments or birds, without being told what these objects are.‍

Supervised Machine Learning: Categories

Two categories of supervised machine learning are Classification and Regression.

Classification: Supervised machine learning can be categorized in a predetermined Yes/No or True/False. The algorithm gives the judgment based on these predetermined data sets only.

Some popular classification algorithms are Random Forest Algorithm, Decision Tree Algorithm, Logistic Regression Algorithm and Support Vector Machine Algorithm.

Regression: Supervised learning technique is used to predict continuous values based on prespecified datasets. Example can be prediction of weather conditions for the upcoming week or month based on prespecified datasets.

Some popular regression algorithms are Simple Linear Regression Algorithm, Multivariate Regression Algorithm, Decision Tree Algorithm, and Lasso Regression

Unsupervised Machine Learning: Categories

Two categories of unsupervised machine learning are: ‍

Clustering: In clustering objects with the most similarities are grouped in one group. This group has no similarity with the objects of other groups.‍

Example: Clustering is customer segmentation where companies club customers on basis of certain criteria like spending habits.

Popular clustering algorithms are K-Means Clustering algorithm, Mean-shift algorithm, DBSCAN Algorithm, Principal Component Analysis and Independent Component Analysis.

Association: This algorithm finds the relationship between variables in a large dataset.

Example: Through the Association method, it can be predicted that the customers who buy one product (bread) are likely to another as well (cheese). One real-world example of the association method is shopping recommendations on e-commerce websites.

Popular association algorithms are K-means clustering, KNN (k-nearest neighbors), Hierarchical clustering, Anomaly detection, Neural Network Principal Component Analysis, Independent Component Analysis, Apriori algorithm and Singular value decomposition.

Supervised vs Unsupervised Learning: Key Differences‍

'Now let’s look at a few differences between supervised vs unsupervised learning. As of now, we know that a supervised learning algorithm is trained on a labeled data set through, at least an initial human intervention. On the contrary, in unsupervised learning, the model is trained on data that has no labeled responses.

The table below provides all the major differences between supervised and unsupervised learning.

Aspect	Supervised Learning	Unsupervised Learning
Data labeling	The algorithm is trained on a labeled data set	No labels are given to the algorithm
Human intervention	Requires human intervention to label the data appropriately	Human intervention is not needed
Accuracy of outputs	Outputs generated are more accurate and trustworthy	Outputs are not as accurate as the algo works without human intervention
Prediction capabilities	Can predict outcomes	Can only group data together
Volumes it can incorporate	Finds it difficult to work on large volumes of data	Can work on large volumes of data at a time
Training requirements	Needs to be first trained with labels to find hidden patterns	Can automatically find hidden patterns

Semi-Supervised Learning: A Hybrid Approach

Semi-supervised learning is used where training dataset contains both labeled and unlabeled data. It is used when its difficult to extract relevant features of data and when you have high volume of data.‍

Example: A semi-supervised learning model can be used on a data set with millions of images where only a thousand of images are labeled. Such as in medical images where only a small amount of trained data can lead to significant improvement is data accuracy.

A radiologist can label a small subset of CT scans for tumors or diseases and then machines can accurately predict which patient requires more medical attention without actually labelling the entire data set. ‍

Self-Learning with Reinforcement Learning:

In this machine learning form, the machine learning algorithm self-learns and improves through hit and trial methods. It gets rewarded with every correct action and gets punished for every wrong action. Unlike previous forms of learning, there is no concept of labeled and unlabeled data.

Unlike supervised, unsupervised and semi-supervised learning models which work on static datasets, the reinforcement learning model works in a dynamic environment. The goal is not to cluster data or label data, but to find the best sequence of actions to generate optimal outcomes.

Reinforcement Learning uses a piece of software called an agent that explores, interacts with and learns from the environment. The agent takes an action. Based on that action, it gets rewarded if that action is appropriate in accordance with the environment or punished if that action is inappropriate.‍

Example: A real-world example of reinforcement learning is when a person looks left and right before crossing the street. By doing so, he gets rewarded by reaching the other side of the street. On the other hand, if he doesn’t take adequate precautions by looking left and right, he gets punished by being hit by a moving vehicle. ‍

Interview Kickstart’s Offerings Related to Machine Learning

If you are planning to take up a role of a machine learning engineer in a top IT company in the world, opt for a comprehensive machine learning course offered by Interview Kickstart. This course covers everything from fundamental concepts to advanced machine learning techniques, and includes hands-on experience with various machine learning projects.

Other notable courses offered by Interview Kickstart are Data Science, Applied Gen AI, Data Analytics, Advanced Gen AI and Full Stack Development to name a very few.

All these courses offer comprehensive training from instructors who themselves are tech leads and hiring managers at top-tier IT companies, making them ideal tech interview prep courses for any AI/ML aspirant.

You can also follow the success stories of candidates who have opted for Interview Kickstart’s courses.

To Sum it Up

Machine learning models are a powerful way to gain data insights that improve our world. The right model of your data depends on the type of data you have and what you want to do with it. Supervised and unsupervised learning is only the first step.

FAQs: Supervised vs Unsupervised Learning

Is ChatGPT An Example of Supervised or Unsupervised Learning?
ChatGPT is an example of a combination of supervised, unsupervised and reinforcement learning with human feedback (RLHF). It uses supervised learning for Fine-tuning with labeled data, unsupervised learning for Initial pre-training on a large corpus of text data and reinforcement learning for further refinement using human feedback.‍

What is Unsupervised Data?
Unsupervised data science focuses on exploring and identifying hidden patterns and structures within unlabeled datasets. There are no output variables to predict. Patterns of data are identified on the basis of relationships between data points themselves.‍

Is Deep Learning Supervised or Unsupervised?
Deep Learning uses algorithms that analyze data with a logical structure similar to human reasoning. This process can be performed through both supervised and unsupervised learning methods.

Is Generative AI Unsupervised?
Generative AI uses a variety of techniques which can involve supervised, unsupervised, or a combination of both learning approaches. The type of generative model and the training methodology used decides on the training model being used.

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Nitin Grover

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Nitin Grover

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The Business Impact of Machine Learning: Real-world Case Studies

What is OpenAI? Everything You Need to Know

Mock Interviews for Generative AI: Essential Practice to Land Top AI Roles

Top Python Scripting Interview Questions and Answers You Should Practice

Complex SQL Interview Questions for Interview Preparation

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