Supervised vs. Unsupervised Learning
Machine learning is already an essential part of how modern organizations and services function. Whether in social media platforms, healthcare, or finance, machine learning models are deployed in various settings. But the steps needed to train and deploy a model will differ depending on the task and available data.
Supervised and unsupervised learning are two types of machine learning model approaches. They differ in how the models are trained and the condition of the required training data. Each system has different strengths, so the task or problem a supervised vs. unsupervised learning model faces will usually differ.
As machine learning becomes increasingly common, it’s essential to understand the core differences between supervised vs. unsupervised learning. If an organization is looking to deploy a machine learning model, the choice will be made by understanding the available data and the problem that needs to be solved. This guide explores supervised vs. unsupervised machine learning, including the main differences in approach, how they are utilized, and examples of both types.
What is supervised learning?
Supervised machine learning requires labeled input and output data during the training phase of the machine learning model lifecycle. A data scientist often marks this training data in the preparation phase before being used to train and test the model. Once the model has learned the relationship between the input and output data, it can classify new and unseen datasets and predict outcomes.
It is called supervised machine learning; at least part of this approach requires human oversight. The vast majority of available data is unlabelled, raw data. Human interaction is generally required to label data ready for supervised learning accurately. Naturally, this process can be resource-intensive, as large arrays of accurately labeled training data are needed.
Supervised machine learning is a predictive model to classify unseen data into established categories and forecast trends and future changes. A model developed through supervised machine learning will learn to recognize objects and the features that organize them. Predictive models are also often trained with supervised machine-learning techniques. Supervised machine learning models can predict outcomes from new and unseen data by learning patterns between input and output data. This could be in forecasting changes in house prices or customer purchase trends.
Supervised machine learning is often used for:
classify file types, such as images, documents, or written words.
We are forecasting future trends and outcomes through learning patterns in training data.
What is unsupervised learning?
Unsupervised machine learning is the training of models on raw and unlabelled training data. It is often used to identify patterns and trends in raw datasets, or to cluster similar data into a specific number of groups. It’s also often an approach used in the early exploratory phase to better understand the datasets.
As the name suggests, unsupervised machine learning is more of a hands-off approach compared to supervised machine learning. A human will set model hyperparameters such as the number of cluster points, but the model will process huge arrays of data effectively and without human oversight. Unsupervised machine learning is therefore suited to answer questions about unseen trends and relationships within data itself. But because of less human oversight, extra consideration should be made for the explainability of unsupervised machine learning.
The vast majority of available data is unlabelled, raw data. By grouping data along similar features or analysing datasets for underlying patterns, unsupervised learning is a powerful tool used to gain insight from this data. In contrast, supervised machine learning can be resource intensive because of the need for labelled data.
Unsupervised machine learning is mainly used to:
Cluster datasets on similarities between features or segment data
Understand relationship between different data point such as automated music recommendations
Perform initial data analysis
Supervised vs unsupervised learning compared
The main difference between supervised vs unsupervised learning is the need for labelled training data. Supervised machine learning relies on labelled input and output training data, whereas unsupervised learning processes unlabelled or raw data. In supervised machine learning the model learns the relationship between the labelled input and output data. Models are finetuned until they can accurately predict the outcomes of unseen data. However, labelled training data will often be resource intensive to create. Unsupervised machine learning on the other hand learns from unlabelled raw training data. An unsupervised model will learn relationships and patterns within this unlabelled dataset, so is often used to discover inherent trends in a given dataset.
So overall, supervised and unsupervised machine learning are different in the approach to training and the data the model learns from. But as a result, they also differ in their final application and specific strengths. Supervised machine learning models are generally used to predict outcomes for unseen data. This could be predicting fluctuations in house prices or understanding the sentiment of a message.
Models are also used to classify unseen data against learned patterns. On the other hand, unsupervised machine learning techniques are generally used to understand patterns and trends within unlabelled data. This could be clustering data due to similarities or differences, or identifying underlying patterns within datasets. Unsupervised machine learning can be used to cluster customer data in marketing campaigns, or to detect anomalies and outliers.
The main differences of supervised vs unsupervised learning include:
The need for labelled data in supervised machine learning.
The problem the model is deployed to solve. Supervised machine learning is generally used to classify data or make predictions, whereas unsupervised learning is generally used to understand relationships within datasets.
Supervised machine learning is much more resource-intensive because of the need for labelled data.
In unsupervised machine learning it can be more difficult to reach adequate levels of explainability because of less human oversight.
Supervised vs unsupervised learning examples
A main difference between supervised vs unsupervised learning is the problems the final models are deployed to solve. Both types of machine learning model learn from training data, but the strengths of each approach lie in different applications. Supervised machine learning will learn the relationship between input and output through labelled training data, so is used to classify new data using these learned patterns or in predicting outputs.
Unsupervised machine learning on the other hand is useful in finding underlying patterns and relationships within unlabelled, raw data. This makes it particularly useful for exploratory data analysis, segmenting or clustering of datasets, or projects to understand how data features connect to other features for automated recommendation systems.
Examples of supervised machine learning include:
Classification, identifying input data as part of a learned group.
Regression, predicting outcomes from continuously changing data.
Examples of unsupervised machine learning include:
Clustering, grouping together data points with similar data.
Association, understanding how certain data features connect with other features.
Here we explore the main applications of supervised vs unsupervised learning, including examples of specific algorithms in action today.