What Is Supervised Learning?

Supervised learning is a form of machine learning that uses labeled data sets to train algorithms. With supervised learning, labeled data sets allow the algorithm to determine relationships between inputs and outputs. As the algorithm works through its training data, it identifies patterns that eventually can refine predictive models or inform decisions during automated workflows. In essence, the labeled data sets act as examples for the algorithm to learn, like a student in a structured classroom.

Supervised learning is the ideal choice for a range of missions and circumstances. If a project has a well-defined goal, supervised learning can help teams finish faster versus using unsupervised learning, where the algorithm ingests an unlabeled data set without parameters or goals and determines patterns and relationships in the data on its own. In supervised learning, labeled data sets act as guideposts for algorithm training.

In addition, compared with other forms of machine learning, training a supervised learning algorithm comes with the advantage of dealing with known quantities, such as features and outcomes. This can speed up the review process, as standard metrics let trainers get a tangible understanding of a project’s current status.

With supervised learning, organizations can gain several benefits. By integrating the ability to efficiently process big data, organizations can identify patterns and insights far faster to inform more timely decisions. In addition, supervised learning algorithms can power task automation efforts, potentially improving and speeding workflows. For example, a machine learning algorithm in a manufacturing operation could train using historic data sets to identify typical maintenance cycles for various pieces of equipment. Then, the system could apply that knowledge to real-time data from sensors that track a tool’s usage and performance. The algorithm then could flag signs of wear or warn of end-of-life for critical parts so that replacements may be ordered before a tool malfunction shuts down a production line.

What Is Supervised Learning?

Supervised machine learning starts by curating labeled training data sets, with inputs and outputs clearly and consistently identified. The algorithm takes in this data to learn relationships; that learning leads to a mathematical model for prediction. The training process is iterative and repeats to refine the algorithm until the model achieves a desired level of accuracy. At that point, different data sets can be used to evaluate and confirm that the model is ready to work with live data.

Supervised learning algorithms generally fall into one of two categories.

Classification: Classification algorithms take data and put inputs into categorized outputs. For example, a finance algorithm for fraud detection will look at a credit card customer’s purchase history and use that data to decide whether a new transaction is likely legitimate or should be flagged for further fraud inspection.

Regression: Regression algorithms use labeled training data sets to identify a best-fitting relationship between inputs and outputs so that mathematical predictions can be made for new inputs. For example, a weather algorithm can take in variables such as season, recent trends, historic patterns, and current environmental metrics to create a forecast output.

Although supervised learning is a proven and effective machine learning approach, it comes with several challenges. Teams should review the following issues before deciding whether to proceed with supervised learning.

Model selection: Supervised learning algorithms range in complexity and resource intensiveness. For example, a decision tree—essentially a flowchart of decision points and possible outcomes—can run with a light footprint yet lacks the capabilities for strict accuracy in a complex area. On the other hand, a deep neural network will require far more resources for both training and production but can eventually do accurate forecasting and much more. Finding the right balance is key to a successful project.

Quality of training data: Any machine learning project requires clean data from quality sources. For supervised training data, that specifically means data with accurate and consistent labeling that’s compatible with other sources used for training. If training data sets aren’t in compatible formats, data integration and transformation techniques must be applied before training, which adds time and expense.

Understanding of project constraints: Factors such as budget, training environment resources, and deadlines can create practical constraints that will dictate the realities of a machine learning project. Because these constraints can affect algorithm selection, teams should identify parameters before starting.

The bottom line is that supervised learning can be the right machine learning approach for projects where labeled data sets are available. Beyond that, teams should understand that supervised learning works best when the goal is accurate predictions or decisions based on identified patterns—think fraud or spam detection, where the algorithm can be trained on examples of correct and incorrect outcomes. Finally, understanding different types of supervised learning models, such as decision trees and linear regression, will inform whether this is the right approach for a specific project.

Supervised Learning FAQs

What is an example of a supervised learning algorithm?

An example of a supervised learning algorithm is the creation of a model that predicts the likelihood of a medical condition based on a patient’s electronic health record. The model is trained on a labeled set of patient data, using factors such as symptoms, age, test results, preexisting conditions, and other factors. That allows the system to intake a patient’s data and identify what, if anything, might fit an undiagnosed medical condition and prompt a closer look.

What is an example of unsupervised learning?

Unlike supervised learning, unsupervised learning algorithms are trained using data sets without labels. The goal of unsupervised learning is to allow the algorithm to explore data and identify patterns on its own. This resulting model then can be applied to incoming data. An example of unsupervised learning is a customer segmentation model, which can take patterns in large data sets of customer usage and purchase history to cluster customers into groups for marketing purposes.

Is CNN supervised or unsupervised?

A convolutional neural network (CNN) is a supervised learning technique trained on labeled data sets for purposes such as image or video analysis, in addition to applications with similar models such as natural language processing. CNNs use multiple layers to separate tasks, such as identifying features/specifics or applying classification, and optimize computational resources.