Supervised vs. Unsupervised Learning: Pros, Cons, and When to Choose

Supervised learning and unsupervised learning are two common types of machine learning models. You can use machine learning in descriptive, predictive, and prescriptive analyses to answer questions, predict events, and guide decisions. Discover the uses of each approach, their pros and cons, and how to decide which is right for your purposes. 

Supervised and unsupervised learning uses

Machine learning, a subset of artificial intelligence (AI), uses algorithms to parse data, gather information, and output predictions or decisions without being specifically programmed to do so. Various disciplines use supervised and unsupervised learning algorithms in machine learning processes, each with its own strengths and best-case uses. 

By understanding how the unique features of each learning algorithm can benefit different functions, you can make informed decisions about how to use these tools to answer questions and guide decision-making. 

Supervised learning

You use supervised machine learning algorithms when you have defined, known output data. This learning method requires labeled input and output data to train the model, which can then make predictions by learning from the provided data set. For instance, supervised learning can perform applications like email spam filtering and object recognition.

Unsupervised learning

You might choose unsupervised machine learning, on the other hand, when the target output is unknown and the data is unlabeled. This type of learning discovers hidden patterns in data. It is commonly used for clustering data points in different groups (such as populations), which can help with tasks like market segmentation. Other applications include anomaly detection, such as detecting faulty equipment or security concerns.

Examples of supervised and unsupervised learning algorithms

Different algorithms work best for different goals. Depending on your industry and what you want to use it for, one type of algorithm may suit your needs. 

Supervised learning examples

Some typical supervised learning algorithms and their applications include:

• Logistic regression: A classification algorithm commonly used when the output variable is binary (e.g., 0/1, yes/no, true/false) or has finite possible answers (e.g., small/medium/large, one/two/three). Examples include determining whether an email is spam or predicting if a learner will pass or fail.

• Decision trees: Decision trees can perform both classification and regression tasks. Decision trees are a great option when interpretability is important, as they are easy to understand and visualize. They can also handle several data types, including continuous, categorical, and data with missing values. You could use decision trees to make business decisions by analyzing and weighing different risks, choices, and goals.

• Neural networks: This is a powerful supervised learning technique designed to duplicate the functions of the human brain. It excels at processing high-dimensional data like images or natural language. For instance, you could use this algorithm in image recognition or language translation applications.

Unsupervised learning examples

Some examples of common unsupervised learning algorithms include:

• K-means clustering: This method is used when you need to segment a data set into distinct groups based on similar characteristics. For example, you might use K-means to segment a customer base for targeted marketing.

• Hierarchical clustering: Similar to K-means, this algorithm can also perform data segmentation. The difference is that hierarchical clustering creates a tree-like model of the data, allowing you to visualize the nested grouping. You could use this method to categorize written documents based on their topics.

• DBSCAN (density-based spatial clustering of applications with noise): You can also use this option for clustering tasks, especially when dealing with spatial data or when you don’t know the number of clusters beforehand. Anomaly detection is just one example of how you might use this method.

• Principal component analysis (PCA): This technique can be used for dimensionality reduction when dealing with multivariate data. It can help visualize high-dimensional data and benefit areas such as gene expression analysis or customer segmentation.

Supervised vs. unsupervised learning: Advantages

Machine learning techniques have become increasingly common in many professional fields. However, each method has pros and cons that may influence whether it is the right choice for your needs. Some typical advantages experienced by users of these algorithms include the following:

Pros of supervised learning

• Produces highly accurate and reliable models (assuming sufficiently high-quality data)

• Performance is easy to measure based on labeled data sets and known outcomes

• Capable of handling a wide array of tasks using both classification and regression

• Ideal for applications where existing data can predict future trends

Pros of unsupervised learning

• Excels at exploring raw, unstructured data and uncovering hidden patterns

• Can handle high volumes of data and generate representations quickly

• Able to simplify complex, high-dimensional data

• Classification is fast

Supervised vs. unsupervised learning: Disadvantages

As with any technical tool, you can also find attributes that may be disadvantageous depending on your needs.

Cons of supervised learning

• Relies heavily on labeled training data and can be time-consuming or challenging to collect

• Prone to overfitting when dealing with training data that is noisy, too large, too small, or complex

Cons of unsupervised learning

• Can sometimes lead to unexpected or suboptimal results due to unlabeled data

• Can be challenging to measure the performance of unsupervised learning models 

• Might be computationally intensive, especially when dealing with large data sets

• Only performs classification tasks

How to choose between supervised and unsupervised learning

Understanding the differences between supervised and unsupervised learning may be essential for effectively leveraging machine learning in your projects. Each type has its place and is instrumental in completing different tasks. Your choice depends on the specific problem, the nature of your data, the tools and time you have, and the objective of your analysis.

Supervised learning is typically easier to implement and evaluate with basic machine learning methods using common programming languages (such as R or Python). Unsupervised learning often requires more complex programming knowledge and skills to work with unclassified information and large training sets.

When to use supervised learning

If you have labeled data and a clear understanding of what you want to predict, supervised learning is the way to go. This makes it suitable for applications like:

• Image recognition

• Customer sentiment analysis

• Spam detection

• Predictive analysis

• Data mining

• Health monitoring

When to use unsupervised learning

If you have a large amount of data but no idea of what the outputs should be, unsupervised learning can explore the data and find structures or patterns. This makes it great for:

• Exploratory data analysis

• Image compression

• Social network analysis

• Detecting anomalies

• Dimensionality reduction

• Identifying customer personas

• Determining market segmentation

Continue learning with Coursera

Consider deepening your machine learning skills with courses from top universities and industry leaders on the Coursera learning platform. As a beginner, consider the Supervised Machine Learning: Regression and Classification course by DeepLearning.AI, or expand your skill set with the Machine Learning Specialization.