white pattern

Supervised vs Unsupervised Learning

white pattern

Try Free for One Month

Unlock insights in unstructured data with computer vision and text mining. Create ML pipelines with assisted modeling.


Supervised vs. Unsupervised Learning; Which Is Best?

Supervised and unsupervised learning models work in unique ways to help businesses better engage with their consumers.

Smart technology is everywhere, permeating almost every aspect of daily life. Consumers have come to expect more information, more automation, faster, all at the click of a button. To keep up, companies must continue to adapt and implement the latest technologies or risk falling behind.

The advancement of Artificial Intelligence (AI) in business has compounded this need. Security systems can turn fingerprint and face scans into biometric data to unlock doors and smartphones. Banking systems can detect unusual purchasing patterns and automatically send a message for human verification of transactions. Voice assistants on smartphones use Natural Language Processing to process audio and return answers to a wide range of requests. All these remarkable technologies are constantly becoming more advanced through the use of Machine Learning (ML) algorithms.

Machine learning is a subset of AI. More specifically, it is an application of artificial intelligence that provides systems with the ability to learn and improve from data. Much the same as humans learn from everyday experiences, ML gradually improves predictions and accuracy over multiple iterations. For ML models, training data is provided from IoT devices, collected from transactions, or recorded from social media. Data science algorithms help sift through, classify, and group information together based on various parameters for these machines. With the data processed and combined, ML can then create models that accurately predict certain human behavior patterns and initiate responses accordingly.

For example, when a customer browses online to purchase their next mobile phone and has narrowed down their choices, the site offers them comparisons with other phones or accessories that purchasers can make at the same time. This model of response is created from data that was processed from earlier similar purchases, enabling the machine to create a model that will help new customers make similar, informed choices.

ML functions on three types of algorithms — Supervised, unsupervised, and reinforced. In reinforcement learning, machines are trained to create a sequence of decisions. Supervised and unsupervised learning have one key difference. Supervised learning uses labeled datasets, whereas unsupervised learning uses unlabeled datasets. By “labeled” we mean that the data is already tagged with the right answer.

Supervised Learning

The supervised learning approach in ML uses labeled datasets that train algorithms to classify data or predict outputs precisely. The model uses the labeled data to measure the relevance of different features to gradually improve model fit to the known outcome. Supervised learning can be grouped into two main types:


A classification problem uses algorithms to classify data into particular segments. An everyday example is an algorithm that helps reject spam for a primary e-mail inbox or an algorithm that lets a user block or restrict someone on social media. Some common classification algorithms include Logistic Regression, K-Nearest Neighbors, Random Forest, Naïve Bayes, Stochastic Gradient Descent, and Decision Trees.


This is a statistical and ML method that uses algorithms to measure the relationship between a dependent variable and one or more independent variables. With regression models, the user can make cause-effect predictions based on various data points. In a business, for example, this could involve predicting advertising revenue growth trajectory. Some common regression algorithms include Ridge Regression, Lasso, Neural Network Regression, and Logistic Regression.

Unsupervised Learning

With unsupervised learning, ML algorithms are used to examine and group unlabeled datasets. Such algorithms can uncover unknown patterns in data without human supervision. There are three main categories of algorithms:


Based on similarities or differences, unlabeled data is grouped using clustering techniques. For example, if a business is working on market segmentation, the K-means clustering algorithm will allocate similar data points to groups that represent a set of parameters. This could group based on location, income levels, age of purchasers, or some other variable.


If a user wants to identify relationships of variables within a dataset, the association method of unsupervised learning is useful. This is the method used to create the prompt — “other customers also looked at”. It is a method ideally suited for recommendation engines. 15 customers purchased a new phone, and they also purchased the headphones to go with it. Therefore, the algorithms recommend headphones to all customers who put a phone in their shopping cart.

Dimensionality Reduction

Sometimes a dataset has an unusually high set of features. Dimensionality reduction helps reduce this number without compromising the integrity of the data. This is a technique that is commonly used before processing data. An example is the removal of noise from an image to enhance its visual clarity.

Differences Between Supervised and Unsupervised Learning

Once the principles of supervised and unsupervised learning are understood, it is simple to understand the differences between them.

The distinction between labeled and unlabeled datasets is the key difference between the two approaches. Supervised learning makes use of labeled datasets to train classification or prediction algorithms. The labeled “training” data is fed in, and the model iteratively adjusts how it weighs different features of the data until the model has been fitted appropriately to the desired outcome. Supervised learning models are far more precise than their counterpart approach. However, they require humans to be involved in the data processing procedure to ensure the labels on the information are appropriate.

An example is that a supervised learning model can predict flight times based on peak hours at an airport, traffic congestion in the air, and weather conditions (besides other possible parameters). But humans have to intervene to label the datasets to train the model on how these factors can affect flight timings. A supervised model depends on knowing the outcome to conclude that snow is a factor in flight delays.

In contrast, unsupervised learning models are constantly working without human interference. They find and arrive at a structure of sorts using unlabeled data. The only human help needed here is for the validation of output variables. For example, when someone shops for a new laptop online, an unsupervised learning model will figure out that the person belongs to a group of buyers who buy a set of related products together. However, it is the job of a data analyst to validate that a recommendation engine offers options for a laptop bag, a screen guard, and a car charger.

Results vs Insights

The goals with supervised and unsupervised learning are different. While the former is about the prediction of outcomes for new data that is introduced, the latter is about getting new insights from massive amounts of new data. In supervised learning, a user will know what results they can expect, whereas in unsupervised learning, they hope to discover something new and unknown.

Varied Applications

Models created from supervised learning are ideally suited to help with spam detection or processing sentiment analysis. These models are also used for things like weather forecasting or predicting changes in pricing. Unsupervised learning is perfectly suited to look for anomalies and outliers of any kind. Supervised learning works well for recommendation engines and understanding customer profiles.

Varied Complexity

When working with supervised learning for model creation in ML, the tools needed are quite simple — often programs such as R or Python suffice. However, unsupervised learning requires computational power to work with massive amounts of unlabeled data.

Disadvantages of Supervised and Unsupervised Learning

As with any technology, both supervised and unsupervised learning models have their disadvantages.

Supervised learning can take a long time to train, and it requires human expertise for label validation — both for inputs and outputs. Working on the classification of big data poses enormous challenges in supervised learning, but once labeled, the results are dependable.

Unsupervised learning sometimes produces completely erroneous results unless there is some form of human intervention to validate the results. Quite in contrast to supervised learning, unsupervised learning can work on any amount of data in real-time but, since the machine teaches itself, transparency on classification is lower. This increases the chances of poor results.

Choosing Between Supervised and Unsupervised Learning

So how does an organization figure out which is the best option for them? The answer lies in the exact context of their requirements and how the data scientists they work with evaluate and organize the bulk of their data. If an organization needs to implement data processing structures, they first need to think about the following things:

They must examine the data and assess if it is labeled or unlabeled.

Does the organization have the time and internal expertise to validate and label? Is the outcome even known?

What are the goals that the organization wants to achieve?

Do they want to solve an existing recurring problem, or would they like the algorithm to discover and solve an unknown problem?

What are the algorithm options?

Does the organization have algorithms of identical dimensionality where they know the attributes of each feature and how many features exist? Can they determine if these features will provide the necessary support to their data volume and its structure?

The decision of whether or not to opt for supervised or unsupervised ML approaches is subject to context, the basic assumptions that can be arrived at on the data on hand, and its final application. The use of either form can change over time as the needs of the organization change.

While an organization may begin training with unlabeled data and therefore use the unsupervised approach, with time, the correct labels will be identified and the machine can switch to supervised learning. This can happen over various stages of labeling. On the other hand, the supervised learning data approach may not be providing the insights required, and unsupervised learning may discover unknown patterns and give deeper insight into business mechanisms.

Getting Started With Machine Learning

So many organizations simply aren’t using ML to their full advantage. The Alteryx Machine Learning Platform is a powerful no-code, low-code tool that automates data processing to help you deploy supervised and unsupervised models. Easily and quickly build complex ML models to solve complex business problems. Get started today and turn your big data into actionable insights and predictions.

Transform Your Analytics

Get ready to unlock hidden insights in your data.
Alteryx Product