Supervised vs unsupervised machine learning: a selection guide
October 31, 2023
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 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
Random forests
Support vector machines (SVM)
Naive Bayes classifiers
Neural networks
Linear regression
Logistic regression
Unsupervised learning algorithms
K-Means clustering
Hierarchical clustering
Principal component analysis (PCA)
Apriori
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?
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.
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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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