Supervised vs. Unsupervised Learning: Types and Use Cases

Machine learning (ML) is changing how organizations operate across industries. Whether you work in healthcare, financial services, marketing, customer service, or any other sector, ML models can help you accomplish various tasks. 

But you must train the models first to get the help you need. The type of tasks you want help with impacts whether you need to train your models using supervised or unsupervised learning. 

Labeled data is critical for supervised learning to work, and businesses use data labeling software to turn unlabeled data into labeled data and build artificial intelligence (AI) algorithms. 

What is supervised learning? 

Supervised learning is a type of machine learning (ML) that uses labeled datasets to identify the patterns and relationships between input and output data. It requires labeled data that consists of inputs (or features) and outputs (categories or labels) to do so. Algorithms analyze the input information and then infer the desired output.

When it comes to supervised learning, we know what types of outputs we should expect, which helps the model determine what it believes is the correct answer. 

What are the types of supervised learning? 

Two of the most commonly used supervised learning methods are classification and regression. 

Classification 

As the name suggests, classification algorithms group data by assigning it to specific categories or outputs based on the input information. The input information consists of features, and the algorithm uses these features to assign each data point to a predefined categorical label. 

One of the most common daily examples of classification is using spam filters in email inboxes. Each email you receive is an input your email provider classifies as “spam” or “not spam” and routes it to the proper folder. In other words, a supervised learning model is trained to predict whether an incoming email is spam using a labeled dataset consisting of legitimate and spam emails. 

To make these predictions, the algorithm analyzes the features of the emails in the dataset, which could include elements like the sender’s email address, subject line, key terms in the body copy, and email length. 

Regression 

Regression algorithms are used to understand the relationship between dependent and independent variables to make future predictions. 

Suppose a car company wants to predict the mileage of a new car model release. The car company can feed a labeled dataset of their previous models with features like engine size, weight, and horsepower to a supervised learning algorithm. The model would learn the relationship between the features and mileage of prior models, allowing it to help predict the mileage of the new car model.

Linear regression 

Linear regression uses linear equations to model the relationship between data points. It strives to find the best-fit linear line between independent and dependent variables to predict continuous variables. For example, you could use a linear regression model to predict the price of a for-sale home using pricing data for comparable homes in the area. 

Logistic regression 

Logistic regression is used to solve classification problems. It can help calculate or predict the probability of an event occurring as either a yes or no. This is called binary logistic regression. For example, the medical profession uses logistic regression to predict whether a tumor that appears on an x-ray is benign or malignant. 

Supervised learning examples 

Some of the most common applications of supervised learning are: 

• Spam detection: As previously mentioned, email providers use supervised learning techniques to classify spam and non-spam content. This is done based on the features of each email (or input), like sender’s email address, subject line, and body copy, and the patterns that the model learns.  
• Object and image recognition: We can train models on a large dataset of labeled images, such as cats and dogs. Then, the model can extract features like shapes, colors, textures, and structures from the images to learn how to recognize these objects in the future.  
• Customer sentiment analysis: Companies can analyze customer reviews to determine their sentiment (e.g., positive, negative, or neutral) by training a model using labeled reviews. The model learns to associate specific words and features with different sentiments and can classify new customer reviews accordingly.

What is unsupervised learning? 

Unsupervised learning is a type of machine learning that uses algorithms to analyze unlabeled data sets without human supervision. Unlike supervised learning, in which we know what outcomes to expect, this method aims to discover patterns and uncover data insights without prior training or labels. 

What are the types of unsupervised learning? 

Unsupervised learning algorithms are best suited for complex tasks in which users want to uncover previously undetected patterns in datasets. Three high-level types of unsupervised learning are clustering, association, and dimensionality reduction. There are several approaches and techniques for these types.

Clustering 

Clustering is an unsupervised learning technique that breaks unlabeled data into groups, or, as the name implies, clusters, based on similarities or differences among data points. Clustering algorithms look for natural groups across uncategorized data. 

For example, an unsupervised learning algorithm could take an unlabeled dataset of various land, water, and air animals and organize them into clusters based on their structures and similarities. 

Clustering algorithms include the following types: 

• Exclusive clustering: As the name suggests, one single data point can only exist in one specific cluster when using this approach as the relationship is exclusive. Exclusive clustering is also referred to as hard clustering.
• Overlapping clustering: Unlike exclusive clustering, overlapping algorithms allow a single data point to be grouped in two or more clusters. Overlapping clustering is also referred to as soft clustering.
• Hierarchical clustering: A dataset is divided into clusters based on similarities between data points. Then, the clusters are organized based on hierarchical relationships. There are two types of hierarchical clustering: agglomerative and divisive.
  • Agglomerative clustering categorizes data in a bottoms-up manner, meaning data points are isolated and then merged as similarities arise until they form a cluster.
  • Divisive clustering takes the opposite approach, a top-down method of dividing clusters based on differences between data.
• Probabilistic clustering: As the name suggests, in a probabilistic clustering model, data points are clustered based on the likelihood that they belong to a distribution. Probabilistic clustering allows objects to belong to one or more clusters. 

Association 

In this unsupervised learning rule-based approach, learning algorithms search for if-then correlations and relationships between data points. This technique is commonly used to analyze customer purchasing habits, enabling companies to understand relationships between products to optimize their product placements and targeted marketing strategies. 

Imagine a grocery store wanting to understand better what items their shoppers often purchase together. The store has a dataset containing a list of shopping trips, with each trip detailing which items in the store a shopper purchased. 

The store can leverage association to look for items that shoppers frequently purchase in one shopping trip. They can start to infer if-then rules, such as: if someone buys milk, they often buy cookies, too. 

Then, the algorithm could calculate the confidence and likelihood that a shopper will purchase these items together through a series of calculations and equations. By finding out which items shoppers purchase together, the grocery store can deploy tactics such as placing the items next to each other to encourage purchasing them together or offering a discounted price to buy both items. The store will make shopping more convenient for its customers and increase sales. 

Dimensionality reduction 

Dimensionality reduction is an unsupervised learning technique that reduces the number of features or dimensions in a dataset, making it easier to visualize the data. It works by extracting essential features from the data and reducing the irrelevant or random ones without compromising the integrity of the original data.

Unsupervised learning examples 

Some of the everyday use cases for unsupervised learning include the following:

• Customer segmentation: Businesses can use unsupervised learning algorithms to generate buyer persona profiles by clustering their customers’ common traits, behaviors, or patterns. For example, a retail company might use customer segmentation to identify budget shoppers, seasonal buyers, and high-value customers. With these profiles in mind, the company can create personalized offers and tailored experiences to meet each group’s preferences.
• Anomaly detection: In anomaly detection, the goal is to identify data points that deviate from the rest of the data set. Since anomalies are often rare and vary widely, labeling them as part of a labeled dataset can be challenging, so unsupervised learning techniques are well-suited for identifying these rarities. Models can help uncover patterns or structures within the data that indicate abnormal behavior so these deviations can be noted as anomalies. Financial transaction monitoring to spot fraudulent behavior is a prime example of this. 

Choosing between supervised and unsupervised learning 

Selecting the suitable training model to meet your business goals and intent outputs depends on your data and its use case. Consider the following questions when deciding whether supervised or unsupervised learning will work best for you: 

• Are you working with a labeled or unlabeled dataset? What size dataset is your team working with? Is your data labeled? Or do your data scientists have the time and expertise to validate and label your datasets accordingly if you choose this route? Remember, labeled datasets are a must if you want to pursue supervised learning.
• What problems do you hope to solve?  Do you want to train a model to help you solve an existing problem and make sense of your data? Or do you want to work with unlabeled data to allow the algorithm to discover new patterns and trends? Supervised learning models work best to solve an existing problem, such as making predictions using pre-existing data. Unsupervised learning works better for discovering new insights and patterns in datasets. 

Supervised vs. unsupervised learning summarized 

Compare supervised and unsupervised learning to understand which will work better for you. 

What did you learn? 

Supervised learning models require labeled training data with an understanding of what the desired output should look like. Unsupervised learning models work with unlabeled input data to identify patterns or trends in the dataset without preconceived outcomes. Whether you choose supervised or unsupervised learning depends on the nature of your data and your goals. 

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