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.

How
supervised
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

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

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

Regression

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

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

Detection

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

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

Feedback
Feedback

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

Algorithms
Algorithms

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

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

Complexity
Complexity

It’s less computationally complex

It has higher compute requirements

Accuracy
Accuracy

Supervised learning models are generally more accurate

Unsupervised learning models can be less accurate

Scenario
Scenario

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: devopedia.org — 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: medium.com — 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: kdnuggets.com — 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: ibm.com — 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: scribbr.com — 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: realpython.com — 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: realpython.com — K-Means Clustering in Python: A Practical Guide

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

Hierarchical clustering

Hierarchical clustering can categorize data points into subgroups based on their similarity (agglomerative clustering) or differences (divisive clustering). Agglomerative clustering can be further divided into single, complete, average, and Ward's linkage based on the method to measure similarity.

Scheme title: Hierarchical methods in unsupervised learning
Data source: devopedia.org — towardsdatascience.com—Hierarchical Agglomerative Clustering Algorithm Example In Python

.02s.02s.02sSingle linkage.04s.04s.04sAverage linkage.04s.04s.04sComplete linkage.04s.04s.04sWard linkage

Principal component analysis (PCA)

PCA is a dimensionality reduction algorithm that narrows the range of features in large datasets to the most relevant ones, minimizing redundancies and making the data easier to visualize. This method projects each data point onto a few principal components represented as vectors and corresponding to essential variables.

Scheme title: Principal component analysis
Data source: Wikipedia — Principal component analysis

-8-6-4-2024681086420-2-4

Apriori

Apriori is a popular association rule algorithm commonly used in relational data mining to spot subgroups of correlated items across wider data sets and highlight general trends. It starts by identifying individual data points and then proceeds to probe others linked to the first by some relationship.

Scheme title: Association via Apriori algorithm
Data source: towardsdatascience.com — Apriori Algorithm for Association Rule Learning. How To Find Clear Links Between Transactions, 2021

NullABCDEABACADAEBCBDBECDCEDEABCABDABEACDACEADEBCDBCEBDECDEABCDABCEABDEACDEBCDEABCDE

Pros and cons of supervised & unsupervised approaches

Supervised learning

Unsupervised learning

Benefits
Benefits
Superior model reliability and consistency due to well-defined features and labels More control over the range of classes in the training data Better explainability and accountability since the model is built on a set of predefined tracks
No need for labeled data and human intervention, resulting in less expensive training Discovering new or emerging trends and relationships which couldn't be labeled before Ability to identify both patterns and outliers, enabling efficient anomaly detection
Challenges
Challenges
Greater preprocessing requirements because of lengthy human-centered labeling Need for multiple training iterations with new data to fine-tune and update the model Existence of under-represented data (anomalies) when you train an algorithm with labeled data points
Superior computing resources required to sift through raw data and uncover hidden patterns Lack of guidance and feedback during model training Its 'black-box' nature, which reduces interpretability and can raise a regulatory concern

Other approaches to machine learning

Semi-supervised learning

A hybrid approach that combines labeled and unlabeled data. The algorithm is trained with a small amount of human-labeled data points as examples that the system can "study" to autonomously label the rest of the training set.
    Pros
    Partially automated and, therefore, faster labeling compared to supervised learning
    Cons
    Potential accuracy issues if the labeled data is not sufficiently representative of the entire set
    Example

    Training a medical imaging system with a portion of labeled examples to identify tumors more accurately

    A trial-and-error approach where an ML system learns to complete a task by running it over multiple iterations and receiving rewards for correct actions and penalties for errors until it succeeds. The algorithm generates several outputs and selects actions that ensure the best result.
      Pros
      No need for data preprocessing, or prior knowledge of the operational scenario
      Cons
      Lack of actual context understanding beyond the task the system is trained for
      Example

      Training autonomous vehicles or robots to move and properly interact with the surrounding environment

      How to choose the best-fitted ML approach?

      How to choose the best-fitted ML approach?

      Consider the following factors to select a suitable machine learning paradigm for your scenario and related challenges:

      • Your goal
        If you search for an answer to known challenges or insights into future outcomes, you should opt for supervised learning. If you aim to explore large sets of raw data to identify problems or potential opportunities you're unaware of, consider relying on unsupervised learning.
      • Your data
        Companies with enough labeled data and the expertise to manage the labeling process can find supervised learning more appealing. When these conditions are missing, unsupervised learning may be a better option.
      How to choose the best-fitted ML approach?

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      Why not both?

      Supervised and unsupervised learning paradigms have much to offer when leveraged as complementary rather than opposing technologies in a variety of ML use cases. Unsupervised learning can discover profitable trends from otherwise ungovernable data sets, while supervised learning serves as a refinement processing layer, honing in on the discoveries unearthed via unsupervised techniques.

      To get the most out of both approaches, rely on Itransition’s expertise in artificial intelligence and machine learning consulting.

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