Putting Supervised and Unsupervised Learning to Work for Your Business

Machine learning can be a tremendously powerful tool for drawing actionable business insights out of big data. But machine learning algorithms differ in how they use data to learn, with important implications for business applications. Understanding how supervised and unsupervised learning work and the differences between them is critical to successfully automating parts of your business.

Supervised Learning

Supervised machine learning requires data scientists to train the algorithm how to output the correct result for a given input dataset. Essentially, this requires feeding the algorithm a large amount of data, along with the conclusions that the machine is expected to draw from that data. Given enough training data, the machine learning algorithm should then be able to come up with the correct conclusions when turned loose on new data.

A schematic example of supervised machine learning. (Source: Big Data Made Simple)

This is a powerful algorithm in the hands of businesses with the bandwidth to train up the algorithm, since it can be applied to tasks ranging from error-correcting financial audits to automating unstructured data entry. At its most complex, supervised machine learning algorithms can be used as part of cognitive automation to enable image recognition and extraction of information from unstructured data.

The downside to supervised learning is that it takes data — a lot of data. More important, that data must be manually curated by humans prior to training the algorithm. Data needs to be labeled according to a defined set of input variables and categorized into a defined set of output possibilities. Although more training data will ultimately lead to a more accurate algorithm, this process is labor-intensive and relies heavily on having a team of data scientists and a stockpile of historical data.

Unsupervised Learning

Unsupervised learning is more complex than supervised learning but opens the doors for a unique set of applications. Unsupervised learning has no training phase; instead, the algorithm is simply handed a dataset and uses the variables within the data to identify and separate out natural clusters.

The advantages to this type of algorithm are twofold. First, it does not require the same labor-intensive data curation that supervised learning does. Second, unsupervised learning can identify patterns that could not or would not be identified by humans, due either to analytical limitations or to biases during analysis.

Customer segmentation is a prime application of unsupervised machine learning. (Source: Inside Big Data)

A prime application of unsupervised learning is customer segmentation. In this case, the algorithm is naturally blind to inherent biases in how a company may look at its own customer demographics. The result is that unsupervised learning can output a unique set of customer segments, with implications for marketing practices.

Summary

Supervised and unsupervised machine learning methods can provide significant benefits to businesses with a wealth of data. The divergence in how these types of algorithms learn creates opportunities to use the supervised and unsupervised learning for distinct applications. While these algorithms are complex, you don’t need an in-house team of data scientists to implement them thanks to automation services from data-driven companies like WorkFusion and others.