Just as human employees can perform tasks with varying degrees of guidance and oversight, machines can learn with or without supervision. However, in the realm of artificial intelligence, the terms supervised and unsupervised don’t connotate whether a bot’s boss is peering at the screen but instead specify a particular data science strategy.
If you’re contemplating unsupervised versus supervised machine learning and wondering how the two methods differ, this blog entry is for you. Here are the answers to commonly asked questions about those terms.
What is unsupervised machine learning?
Supervised and unsupervised learning are two distinct ways to approach machine learning, i.e., the process of showing a model how to make inferences, according to Amazon Web Services. Unsupervised machine learning involves providing an algorithm with unlabeled data. Subsequently, the algorithm independently pinpoints relationships and patterns.
Unsupervised learning is typically leveraged for three types of work, according to IBM:
- Association: This approach leverages rules to establish relationships between variables. This sort of machine learning drives recommendation algorithms like the ones that Netflix and Amazon utilize.
- Clustering: This method involves grouping data according to differences or similarities. Use cases for this strategy include image compression and market segmentation.
- Dimensionality reduction: This function decreases data inputs without sacrificing data integrity. For example, an autoencoder enhancing images by removing noise is practicing dimensionality reduction.
Unsupervised learning makes sense when you don’t have labeled data available and want to discover anomalies or relationships between variables. An unsupervised approach is best if you want insights from new data. For example, IBM states that this type of machine learning can be used to create customer personas or for medical imaging.
Unsupervised learning models are more complex than supervised algorithms, require large datasets for training, and can potentially produce off-base results if humans don’t validate output variables.
What is supervised machine learning?
Supervised machine learning involves training a model with labeled datasets that serve as an example, teaching the program to make predictions, according to the United States Artificial Intelligence Institute. As IBM explains, this approach involves supplying inputs and outputs humans have labeled; that’s the supervised element.
Supervised machine learning is typically applied to two types of tasks:
- Regression: This method entails establishing relationships between dependent and independent variables and making numerical projections based on various data points. For example, you could utilize a regression model to forecast sales revenue for your business.
- Classification: This strategy involves sorting data into categories. For instance, a classification model might flag spam messages and store them in a specific folder in an email application.
While supervised learning isn’t as complicated as the unsupervised approach, it can prove time-intensive because of the extensive training the models require, IBM notes. Additionally, you must know how to label the input and output data properly.
Unsupervised vs. supervised machine learning: Which approach is best?
The decision to utilize an unsupervised versus supervised approach depends on the specific situation and what you want to accomplish. If you’re trying to solve a problem with a known outcome (e.g., classifying emails as suspicious or safe) and have labeled data on hand, supervised is the way to go, according to AWS.
On the other hand, if you want to identify patterns, flag anomalies, or cluster similar variables, and your data is unlabeled, an unsupervised model makes more sense. If some of your data is labeled, but most isn’t, you can also utilize a combined approach called semi-supervised learning.
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