Supervised vs unsupervised
machine learning: a selection guide

Supervised vs unsupervised machine learning: a selection guide

October 31, 2023

Aleksandr Ahramovich
by Aleksandr Ahramovich, Head of AI/ML Center of Excellence
Supervised and unsupervised learning determine how an ML system is trained to perform certain tasks. The supervised learning process requires labeled training data providing context to that information, while unsupervised learning relies on raw, unlabeled data sets.

Explore how machine learning experts leverage the strengths of these approaches to address specific business challenges better and help organizations build best-fitted ML models.

machine learning works

Supervised learning means training a machine learning algorithm with data that contains labels detailing the target value for each data point. Labeled datasets provide clear examples of inputs and their correct outputs, enabling the algorithm to understand the relationship between them and apply this knowledge to future cases.  The examples of tasks for supervised learning are classification, regression, and detection.


Classification tasks involve dividing data points into specific categories depending on their features.

Classifying incoming emails into “spam” and “not spam”.

Supervised vs unsupervised learning: key differences

Besides the major distinction between using labeled or unlabeled data, the two approaches have other significant differences, as pointed out by Martin Keen, a Master Inventor at IBM.

Supervised learning

Unsupervised learning

Training data

Supervised learning

Unsupervised learning

The algorithm is trained with labeled data sets

The algorithm is trained with unlabeled data sets


Supervised learning

Unsupervised learning

Easy to measure the system’s quality during the model training due to reference data availability

In most cases, you get user feedback only after the system is implemented

Human involvement

Supervised learning

Unsupervised learning

It requires direct intervention to label data

Doesn’t require manual data labeling, but model training still involves human supervision


Supervised learning

Unsupervised learning

Random forests, support vector machines, linear regression, NN, etc.

K-Means clustering, PCA, autoencoders, Apriori, NN, etc.


Supervised learning

Unsupervised learning

It’s less computationally complex

It has higher compute requirements


Supervised learning

Unsupervised learning

Supervised learning models are generally more accurate

Unsupervised learning models can be less accurate


Supervised learning

Unsupervised learning

You know both the input and the corresponding output

You work with unclassified data and the output is unknown

Supervised & unsupervised machine learning use cases

The peculiarities of supervised and unsupervised learning make them ideal for different applications and business scenarios. Here are some examples.

Supervised machine learning use cases

Sentiment analysis

Analyzing user interactions on social media and online platforms to assess their attitude towards topics, products, or brands and refine marketing campaigns.

Weather forecasting

Processing satellite imagery and radar measurements to identify weather patterns and generate precipitation maps more accurately than via statistical models.

Forecasting stock price fluctuations and market volatility based on financial trends and corporate earnings to build more balanced portfolios while minimizing risk.

Calculating the potential value of a real estate property based on its features and location to ensure more profitable investments.

Demand forecasting

Monitoring economic conditions, seasonality-related purchase patterns, and other factors to predict upcoming sales trends and optimize restocking operations.

Face recognition

Detecting and isolating persons in pictures and videos based on their biometric data to classify multimedia content and automate tagging.

Speech recognition

Processing audio inputs and interpreting natural language to power chatbots, moderate online content, and enable real-time transcriptions or translations.

Probing radiological images and other sources to identify tumors, traumas, or other conditions and enable accurate diagnoses.

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ML algorithms used in supervised and unsupervised models

Data scientists and ML engineers can count on a wide selection of algorithms to perform supervised and unsupervised learning tasks. These are some of the most popular ones.

Supervised learning algorithms

Decision trees

A decision tree is a classification algorithm for mapping the branches of possible outcomes from an initial starting point. The calculations result in a graph that's easy to understand and explain but requires a level of human-generated insight and interpretation at each node of the branch.

Scheme title: A decision tree
Data source: — Decision Trees for Machine Learning

Decision nodeDecision nodeDecision nodeLeaf nodeDecision nodeLeaf nodeLeaf nodeLeaf nodeLeaf nodeSub-tree

Unsupervised learning algorithms

K-Means clustering

K-Means is a clustering algorithm that assigns data points to 'K groups'. The K value is the volume of identifiable clusters in a dataset based on their similarity. A higher K value means that more groups are identified, leading to more diverse outcomes and inferred relationships between the data points.

Scheme title: K-Means clustering
Data source: — K-Means Clustering in Python: A Practical Guide

predicted cluster:01234true label:PRADLUADBRCAKIRCCOAD-15-10-505101520component_2151050-5-10-15component_1