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Supervised vs. Unsupervised Learning | Built In

Supervised learning and unsupervised learning are machine learning processes that train AI models to recognize patterns, make predictions and improve their performance over time.

Supervised learning relies on labeled datasets that show clear relationships between inputs and outputs, while unsupervised learning uses unlabeled data, allowing the model to discover patterns on its own — without “supervision.”

• Supervised learning uses labeled datasets, while unsupervised learning works with raw, unstructured data.
• Supervised learning requires more upfront human involvement than unsupervised learning.
• Unsupervised learning is more computationally complex than supervised learning.
• Supervised learning is best suited for problems where the expected results are clearly defined, while unsupervised learning is best for exploratory data analysis.

There are many other nuances between supervised and unsupervised learning, with one tending to perform better than the other depending on the use case. But both play an important role in teaching models to analyze information and produce accurate results.

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Supervised learning is a form of machine learning that uses labeled datasets, meaning both input and output data are tagged with descriptive information.

For example, in a set of animal images, each picture is labeled depending on whether it depicts a dog, cat, bird, rabbit, horse and so on. Then, when the model is tasked with identifying a horse or differentiating between a cat or a dog, it is fed the correct answer — over and over again. This iterative process allows the model to gradually improve to the point where it is capable of making accurate decisions based on new data fed into it.

The input data is divided into features — measurable variables that provide additional information to the model — and the output data is grouped into specific categories using labels. This teaches the model what elements it needs to pay attention to in order to make correct identifications, comparisons and predictions. In the case of animal images, features might include things like size, color, breed and ear shape, while labels might be the species of each animal, like dog, cat and rabbit.

Supervised learning is primarily applied to two kinds of problems: classification and regression.

• Classification is the process of organizing data into specific categories, which involves recognizing common features among data points and deciding how those data points should be labeled or defined. For example, a model must consider features like ear shape, nose shape, size and fur length when classifying cats and dogs. Some classification algorithms include decision trees, random forest models, support vector machines and K-nearest neighbor.
• Regression is the process of understanding the relationships between variables to make predictions. For example, factors like a dog’s breed, size, weight, diet and habitat can be used to help predict its life span. Some regression algorithms include linear regression, logistic regression, multiple regression and polynomial regression. 

Supervised learning has all kinds of real-world use cases, including:

• Inbox Spam Detection: These systems use natural language processing to sift through email inboxes and organize incoming messages as “spam” or “not spam” according to factors like formatting and word choice.
• Image Classification: These systems are trained on labeled images, allowing them to accurately classify new images. This can be valuable in applications ranging from object identification to medical diagnostics.
• Speech Recognition: These systems are trained on both audio recordings and their corresponding transcripts to understand the relationship between spoken language and text. This can be applied to AI assistants, customer service, language translators and more.
• Churn Prediction: By analyzing data like purchase history, service usage and billing, these systems predict the likelihood of a particular customer churning, anticipating whether they will continue doing business with a company or not.

Unsupervised learning is a form of machine learning where a model is trained on raw, unstructured data that has no predefined features or labels. Rather than being told the relationships between input and output data, the model finds hidden patterns and intrinsic structures independently, without human intervention. 

Unsupervised learning is useful when the commonalities within a dataset are not immediately obvious or clear. For example, a business can collect customer data (purchasing behavior, browsing history, location, gender, age, etc.) and group similar customers together, enabling them to make targeted ads and emails, and even predict future behavior.

Unsupervised learning is typically used in three kinds of tasks: clustering, association and dimensionality reduction. 

• Clustering is a data mining technique that groups — or clusters — data based on their similarities. It is comparable to classification, only the data is unlabeled. For example, when patients in a clinical trial report the frequency and severity of their symptoms, researchers can use a clustering analysis to group patients together based on their treatment responses. Some common clustering algorithms include k-means clustering, hierarchical clustering and Gaussian mixture model.
• Association involves identifying relationships or patterns among variables in a dataset, especially how often specific items occur together. A common example is market basket analysis, which seeks to find products that customers frequently buy together (printers and ink cartridges, peanut butter and jelly, bicycles and helmets) — similar to the “Frequently Bought Together” feature on e-commerce sites.
• Dimensionality reduction is the process of reducing the amount of variables in a dataset to a more manageable number, while also preserving the integrity of the data. This is often done in the pre-processing stage, like when autoencoders remove noise from visual data to improve the quality of AI-generated images. The most common algorithm for dimensionality reduction is principal component analysis.

Unsupervised learning is applicable in many scenarios, including:

• Recommendation Engines: By identifying unique patterns in a user’s streaming activity, these systems can recommend what movies and television shows they should watch next.
• Customer Segmentation: These systems analyze customer data and organize individuals into subgroups based on common characteristics and habits (demographics, behavior, preferences, etc.), helping to produce more targeted outreach and personalized recommendations.
• Anomaly Detection: These systems are used to identify unusual patterns or outliers in datasets that may indicate a problem, such as fraud in financial transactions, equipment failures in manufacturing facilities and anomalies in medical images.
• Computer Vision: Unsupervised learning allows these systems to interpret and understand visual information from the world without needing labeled datasets as examples. By grouping similar features like color, shape and texture, these models can automatically identify important parts of an image, allowing them to perform tasks like facial recognition and machine vision.

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The use of labeled versus unlabeled data causes many other differences between supervised learning and unsupervised learning:

• Levels of Human Involvement: Supervised learning requires more upfront human involvement compared to unsupervised learning, as it relies on humans to label all of the data before the model can learn. 
• Computational Complexity: Supervised learning is a simpler approach to machine learning and is typically performed using common programming languages like R and Python. Unsupervised learning tends to be more computationally complex, requiring massive datasets and powerful processing tools.
• Applications: Supervised learning is used for tasks like spam detection, sentiment analysis and weather forecasting. Unsupervised learning is used for tasks like anomaly detection, customer segmentation and recommendation engines.
• Strengths: Supervised learning is best for problems where the expected results are clearly defined, and tends to be more accurate than unsupervised learning. Meanwhile, unsupervised learning is best for exploratory data analysis, where the goal is to discover hidden patterns and relationships in the data.
• Drawbacks: Supervised learning can be a time-consuming process, requiring humans to label large quantities of data. Although it is less labor-intensive to start, unsupervised learning tends to be less accurate than supervised learning unless there is a human validating the outputs.

While both supervised learning and unsupervised learning serve a vital function in the development of artificial intelligence, each contributes in unique ways. 

Supervised learning is best used when the data is labeled and the goal is to predict a specific outcome. This might include what tomorrow’s temperature will be, whether or not a customer will churn or if an incoming email is spam. It’s best to use this approach in situations when there are clear inputs and corresponding outputs, which allows the model to learn from past data and make accurate predictions on new data it hasn’t seen before. 

Unsupervised learning is more helpful in finding new patterns and relationships in unlabeled data. It is used in exploratory data analysis, which involves examining datasets to discover hidden trends and groupings without predefined categories or outcomes. It is also effective in identifying data points that deviate from the norm within a larger dataset, without needing labels to indicate exactly which points are anomalous. 

Because supervised learning requires humans to manually label data, it results in more oversight over the model training process. It’s true that this can be time-intensive and less efficient than unsupervised learning. But if a team is looking to prioritize transparency in its AI solutions, then supervised learning offers a greater degree of control over the development of machine learning models and allows teams to better explain how models make decisions. 

The ability of unsupervised learning to discern general trends among unlabeled data makes it ideal for initial analysis of new data. It can group data into clusters, share general insights and label data accordingly. This now-labeled data can then be used later on to conduct supervised learning and fine-tune models as needed.

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It’s also possible to have the best of both worlds. Semi-supervised learning is a hybrid approach that combines elements of supervised and unsupervised learning. It begins by training a model on a small amount of labeled data and has it make predictions on a larger amount of unlabeled data. The model is then trained repeatedly on labeled and unlabeled data, improving its knowledge and performance each time. 

Semi-supervised learning is most effective in situations where adding a little bit of labeled training data can greatly enhance performance, such as classifying medical images. It can also be used for analyzing speech, sorting internet content and studying protein sequences. 

Semi-supervised learning is just one type of hybrid learning. Below are a few other hybrid approaches in machine learning that are good to know: 

• Transfer Learning: Transfer learning refers to taking knowledge learned from one task and applying it to a new task. It can involve pre-training a model on a large unlabeled dataset and then fine-tuning it on smaller labeled datasets for more specialized tasks. 
• Ensemble Learning: Ensemble learning uses the abilities of multiple models to make more accurate predictions, as opposed to just one. It can be used for hybrid learning by combining ML models trained on labeled and unlabeled data. 
• Hybrid Deep Learning: Deep learning mimics the way the human brain uses neural networks to learn tasks. A deep neural network can extract features from unlabeled data to provide more information that classic ML models can use later on for training. 

Supervised learning and unsupervised learning have many differences, including:

• Supervised learning models use labeled data sets, while unsupervised learning models work with unlabeled data.
• Supervised learning requires more human influence than unsupervised learning.
• Unsupervised learning is more computationally complex than supervised learning.
• Supervised learning is best suited for problems where the expected results are clearly defined, while unsupervised learning is best at exploratory data analysis and discovering new patterns.

A common example of supervised learning is spam detection systems, which sift through email inboxes and classify incoming messages as “spam” or “not spam” — taking into account factors like formatting and word-choice.

A common example of unsupervised learning is customer segmentation, which involves analyzing customer data and organizing individuals into subgroups based on their common characteristics and habits (demographics, past behavior, preferences. etc.). This can be used to help businesses create more targeted ads and marketing emails, provide more personalized recommendations — and even predict customer behavior.

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