Difference between Supervised & Unsupervised Learning

Supervised vs Unsupervised Machine Learning Problems

Supervised and unsupervised learning are two different common types of machine learning tasks that are used to solve many different types of business problems. Supervised learning uses training data with labels to create supervised models, which can be used to predict outcomes for future datasets. Unsupervised learning is a type of machine learning task where the training data is not labeled or categorized in any way. For beginner data scientists, it is very important to get a good understanding of the difference between supervised and unsupervised learning. In this post, we will discuss how supervised and unsupervised algorithms work and what is difference between them.

You may want to check my post on what is machine learning to get a detailed view of different concepts.

Supervised vs Unsupervised Learning Tasks

The following represents the basic differences between supervised and unsupervised learning are following:

  • In supervised learning tasks, machine learning models are created using labeled training data. Whereas in unsupervised machine learning task there is no labels or category associated with training data.
  • Supervised learning models help predict outcomes for future data sets, whereas unsupervised learning allows you to discover hidden patterns within a dataset without the need for human input.

Let’s try and understand the details of supervised and unsupervised learning with the help of examples.

What is Supervised learning?

Simply speaking, supervised learning is a type of machine learning task where the training data is labeled. This supervised machine learning task can be used to predict outcomes for future datasets that are similar to the labeled datasets. Supervised models use labels, which act as a guide to help create an accurate model. The below represents examples of supervised learning problems:

supervised learning machine learning applications examples.jpg

Note some of the following in the above diagram representing supervised learning problem:

  • Input represents the training data without a label
  • The output represents the label found in the training data. Note that the label column represents the response or dependent variable. The variable that needs to be predicted.
  • The application represents details on real-world applications.

Here is the detail of some of the supervised learning problems listed in the above diagram:

  • Housing price prediction: Supervised learning can be used to predict housing prices for homes using historical sold data and price attributes.
  • Whether the user will click on an ad: Supervised learning can be used to predict whether a user will click on an ad or not. This supervised machine learning task uses historical data of prior users’ actions and attributes from the advertisement campaign as input variables, which are then classified into two classes: “click (1)” and “not click (0)”
  • Image classification: Supervised learning can be used to classify images into one of the many different categories. In the above diagram, 1000 categories are shown. This supervised machine learning task uses training data with labeled images in order to create supervised models that can predict which category an image belongs in based on its features.

What is Unsupervised learning?

Unsupervised learning is another common machine learning task where there are no labels associated with the training data. This unsupervised machine learning task is often used to discover hidden patterns or correlations within a dataset, which can be very useful for business owners who want to understand their customers better and make more informed decisions based on this information. The following represents some of the examples of unsupervised learning problems:

  • Customer segmentation: Unsupervised learning is often used to segment customers and groups of similar behavior together. By using this unsupervised machine learning task, companies can create segments within their customer base that represent different types of consumers with distinct needs or criteria based on common characteristics such as demographics or interests.
  • Market basket analysis: Market basket analysis uses association rules to discover hidden product relationships that are often not apparent to the customer. This unsupervised machine learning task can be used by businesses to understand their customers’ shopping habits and identify which products are commonly purchased together in order to optimize pricing or product placement on shelves.
  • Document categorization: Unsupervised learning is often used to automatically categorize documents into different topics or categories. This unsupervised machine learning task allows businesses to understand the types of content that their customers are sharing, which can help them establish a social media presence and gather more information from online sources.
  • Marketing campaign optimization: Unsupervised learning can be used to optimize campaigns by grouping customers into different categories based on their interests or purchase history. This unsupervised machine learning task is often performed with segmentation in mind, which helps companies understand how best to speak to each of their customer groups and deliver the right message at the right time.

The following is a self-explanatory picture representing what is supervised and unsupervised learning techniques and how are they different.

Supervised vs Unsupervised Machine Learning Problems

Figure 1. Supervised vs Unsupervised Machine Learning Problems

Pay attention to some of the following:

  • Supervised learning: In supervised learning problems, predictive models are created based on an input set of records with output data (numbers or labels). Based on the outcome/response or dependent variable, supervised learning problems can be further divided into two different kinds:
    • Regression: When the outcome or response variable is a continuous variable (numeric or number), it can be called a regression problem.
    • Classification: When the outcome or response variable is a discrete variable (labels), it can be called classification problems.
  • Unsupervised learning: In unsupervised learning, patterns or structures are found in data and labeled appropriately.

Supervised and Unsupervised Learning Algorithms

The following diagram represents information in relation to algorithms that can be used in the case of supervised and unsupervised machine learning.

Supervised vs Unsupervised Machine Learning Algorithms

Figure 2. Supervised vs Unsupervised Machine Learning Algorithms

Pay attention to some of the following:

  • Supervised learning algorithms
    • Regression: Linear regression, Support vector regression (SVR), ensemble methods, decision trees, neural networks
    • Classification: Support vector machine (SVM), discriminant analysis, Naive Bayes, K-Nearest Neighbours (KNN)
  • Unsupervised learning algorithms
    • Clustering: K-means, K-medoids, Hierarchical, Gaussian mixture, neural networks, hidden Markov model

Summary

In this post, you learned (visually) about what is supervised and unsupervised learning and how are they different. Supervised and unsupervised machine learning are two different types of tasks that can be used to extract useful information from labeled and unlabeled data respectively. Supervised learning happens when there are labels associated with the training dataset, whereas in unsupervised learning, there are no labels or categories given to the training data. Supervised learning often helps predict outcomes for future datasets while unsupervised allows you to find hidden patterns within a dataset without human intervention. Both supervised and unsupervised machine learning tasks have many different uses depending on what your business needs may be.

Ajitesh Kumar
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Ajitesh Kumar

I have been recently working in the area of Data Science and Machine Learning / Deep Learning. In addition, I am also passionate about various different technologies including programming languages such as Java/JEE, Javascript, Python, R, Julia etc and technologies such as Blockchain, mobile computing, cloud-native technologies, application security, cloud computing platforms, big data etc. I would love to connect with you on Linkedin and Twitter.
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