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”.


Regression enables predicting outcomes based on historical data by finding correlations between dependent and independent variables.

Predicting a new customer’s paycheck based on a conversion history and behavior of each customer in the online store.


Detection tasks imply identifying objects on an image or video. Object detection can spot multiple objects within an image.

Detecting vehicles, road signs, or people on road images; detecting concerning areas on X-Ray images.

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
Training data

The algorithm is trained with labeled data sets

The algorithm is trained with unlabeled data sets


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
Human involvement

It requires direct intervention to label data

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


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

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


It’s less computationally complex

It has higher compute requirements


Supervised learning models are generally more accurate

Unsupervised learning models can be less accurate


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.

Unsupervised machine learning use cases

Identifying abnormal conditions and features which can lead to potentially harmful scenarios, such as fraud, medical issues, and machine breakdowns.

Monitoring users on social media to understand their interests and target them with customized ads, improving conversions and driving sales.

Analyzing purchase and browsing history, reviews, and likes of online platform users to provide them with tailored product or content suggestions, facilitating cross-selling.

Noise reduction

Cutting out irrelevant variables in the data to remove noise from visual content and improve picture or video quality.

Mixed use cases

The choice for supervised vs unsupervised model depends on data available for this task.

Customer segmentation

Grouping customers into buyer personas for efficient market research based on their behavioral patterns, including past purchases and online interactions.

News sections

Scanning news articles from major online media outlets to group content into subcategories based on the country, topic, and other metrics.

Spam detection

Assessing whether or not an email is spam to filter undesired or harmful content and prevent phishing or other types of fraud.

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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

Random forests

A random forest combines multiple decision trees and averages the likelihood of an outcome. It’s less easy to visualize than a decision tree. Still, it minimizes overfitting when the machine learning model and the data become so tailored to each other that the model won’t perform properly on other datasets.

Scheme title: A random forest
Data source: — Machine learning. Decision trees and random forest classifiers

Decision treeDatasetPredictionPredictionPredictionMajority vote takenFinal prediction made

Support vector machines (SVM)

The SVM approach could be represented as a 2D or 3D graph depending on the number of data instances to classify. Then it constructs a boundary, known as a hyperplane, between different types of identified data. The support vectors are the data points of the greatest correlation between two different classes and define these boundaries.

Scheme title: Support Vector Machine
Data source: — Support Vector Machines: a simple explanation

The data points nearest to the boundary in an SVM define the hyperplane – the defining division between two groups: yxSupport vectors

Naive Bayes classifiers

A naive Bayes classifier is an efficient and scalable routine for classification based on Bayes’ theorem, a method of calculating probabilities from historical data. This algorithm can be an adroit and economical solution for reliably-labeled datasets, making it a good first approach to consider when developing a supervised architecture.

Scheme title: A Naive Bayes classifier portrayed as a Bayesian Network
Data source: Wikipedia — Naive Bayes classifier

Neural networks

Neural networks are robust, multi-layered architectures in most cases related to the subset of ML known as deep learning. They can iterate through very high volumes of data in order to discern hidden relationships and classify the data successfully. However, this approach is time-consuming and can require a great deal of experimentation.

Scheme title: Deep neural network
Data source: — Neural Networks

Input layerHidden layer 1Hidden layer 2Output

Linear regression

Linear regression maps the correlations between a dependent variable and one or more independent variables. The goal is to establish whether there is a governing relationship between these two factors and whether that relationship can be expressed algorithmically to predict future outcomes.

Scheme title: A linear regression
Data source: — A step-by-step guide to linear regression in R

y = 0.2 + 0.71 x2468Happiness score (0 to 10)86420Income (x$10,000)

Logistic regression

Similarly to linear regression, logistic regression identifies relationships between input data and output data. Instead of being used on continuous, measurable variables, however, it’s leveraged when the seed variable is binary (i.e. it might be one thing or another), generating a more complicated type of graph called a sigmoid.

Scheme title: Logistic regression
Data source: — Logistic Regression in Python

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