Supervised and unsupervised learning are both subsets of machine learning. But, while related, they work very differently. In this article, we’ll consider those differences as well as if one type of machine learning is inherently better than the other.
First, what is the main difference between supervised and unsupervised learning? Supervised learning uses labeled data to train algorithms to predict outcomes, while unsupervised learning uses unlabeled data to predict outcomes.
To illustrate, imagine you wanted to train an ML algorithm to identify desks using supervised learning. This task is ideal for supervised learning since you’re trying to achieve a specific outcome. You’d feed it labeled images, and once the algorithm is sufficiently trained (possibly at about 1,000 images), you can set it to work identifying similar images in your application.
On the other hand, unsupervised learning doesn’t need labeled data. However, it wouldn’t be ideal for the previous example since unsupervised learning performs better when you’re trying to uncover unknown connections. For example, using the same data set (desk images), you might uncover patterns like color and style preferences, and use these insights to better serve your customers. After considering these examples, you may wonder if one type of machine learning is inherently better than the other.
Align your machine learning approach to your goals
You wouldn’t use a hammer to do the job of a screwdriver and vice versa. Similarly, supervised and unsupervised learning aren’t iterations or upgrades to outdated technology but different tools for different jobs. This difference makes it essential to figure out your goals before choosing an ML approach.
Ideal scenarios
- Supervised learning is best suited for projects where you want to achieve a specific outcome. This type of learning will prioritize the accuracy of your results.
- Unsupervised learning is best when you want to extract insights from large amounts of data but don’t have a specific goal. These projects tend to produce unexpected insights that may provide a competitive edge.
Shortcomings
- Supervised learning requires labeled data which can be time-consuming and labor-intensive to create. Labeling the data necessary also requires specialized talent.
- Unsupervised learning can produce inaccurate results if you don’t have an expert to validate the variables. Additionally, this type of learning requires a more robust computing infrastructure.
Both approaches to machine learning have their strengths and weaknesses. Therefore choosing the right one requires you to align its strengths with your project’s needs. Consider what type of data you have available (labeled vs. unlabeled), what kind of problem you’re trying to solve (well-defined vs. open-ended), and the talent you have access to (experts for labeling, data scientists, etc.).
Preparing your organization for machine learning
Supervised and unsupervised learning can be invaluable tools for organizations leveraging data in their organization. These machine learning methods power everything from speech recognition and forecasting market fluctuations to product recommendations and market segmentation. As organizational data grows, ML tools become more critical to a business’s ability to analyze and interpret their data — since it becomes a metaphorical paperweight without an efficient way to analyze data.
Regardless of the machine learning methods you choose to employ, it’s crucial that your computing infrastructure can handle the load. Equus Compute Solutions has been helping organizations design, deploy, and manage HPC infrastructure for over three decades. Together we can design hardware solutions that fit your business’s unique needs and are ready to scale. Contact us to learn more.
