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”.
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
Feedback
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
Algorithms
Supervised learning
Unsupervised learning
Random forests, support vector machines, linear regression, NN, etc.
K-Means clustering, PCA, autoencoders, Apriori, NN, etc.
Complexity
Supervised learning
Unsupervised learning
It’s less computationally complex
It has higher compute requirements
Accuracy
Supervised learning
Unsupervised learning
Supervised learning models are generally more accurate
Unsupervised learning models can be less accurate
Scenario
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.
Stock market prediction
Forecasting stock price fluctuations and market volatility based on financial trends and corporate earnings to build more balanced portfolios while minimizing risk.
Real estate appraisal
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.
Medical imaging
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.