Share on facebook
Share on linkedin
Share on twitter
Share on facebook
Share on linkedin
Share on twitter

What Is Supervised Learning?

Broadly speaking, there are two types of machine learning algorithms: supervised and unsupervised learning. Supervised learning is the more common of the two, and is typically easier to implement than unsupervised learning.


What Is Supervised Learning?

Supervised learning algorithms are designed to learn by example. They are used when the human practitioner knows the answer to a problem, and wants to train the AI to be able to find it out. It is like learning with the assistance of a teacher, guiding the algorithm towards the ‘correct’ answer, as opposed to an unsupervised learning algorithm, which is like a child learning on their own by experimentation and trial and error.

To train a supervised learning algorithm, you will need to pair a set of inputs with specific outputs. The algorithm will then search for patterns within the inputs to correlate with the outputs. Based on this training data, the supervised learning algorithm can then take in unseen inputs and determine which label to assign them. The aim? To predict the correct label for newly presented input data in order to categorize it and make sense of it.

Supervised learning:

  • · Is simpler and more common than unsupervised learning
  • · Is used when the human practitioner knows the answer, and wants to train the AI
  • · Classifies and processes data using machine language
  • · Uses labeled data (i.e. that which has been classified)


Types of Supervised Learning Algorithms

There are two main types of supervised learning algorithms.

· Classification techniques

A classification technique asks the algorithm to predict a discrete value, in order to identify the input data as the member of a particular class or group. it is very good at recognizing patterns, which makes it ideal for uses such as image recognition. Using a training dataset of animal images, for example, each photo can be pre-labeled as a dog, cat, or guinea pig, and the algorithm is then evaluated by how accurately it can identify new images of these same animals.

Some examples of classification techniques include:

  • · Logistic regression
  • · Linear discriminant analysis
  • · K-nearest neighbors
  • · Trees
  • · Neural networks
  • · Support vector machines

· Linear regression

Linear regression involves using continuous data. Think of it like an algebra problem: given that you know the value of x, what is the expected value of the y variable?

That is a very simple example. A more complex case would be one that involves many variables, like an algorithm that predicts the price of an apartment. It would need to consider variables like location, size in square footage, aspect, proximity to public transport and local facilities, the socio-economic demography of the area, what other similar properties recently sold for, current market conditions, and many more.


What Are the Common Use Cases of Supervised Learning?

1. Recommendation engines

Recommendation engines are increasingly being used in e-commerce – they generate those ‘often bought together’ or ‘you might also like’ product suggestions that appear either on the product page or as part of the checkout process. Buying a coffee maker? You will need these filters. Picking up a set of 10kg dumbbells? Some lighter ones to accompany them will help you get more from your workout.

These are typically generated by AI models created through supervised learning. By analyzing what customers buy or even just browse, the algorithm can find other items that people often go on to purchase. Once it has identified these common purchasing patterns, it can recommend them to new customers who seem like they are heading down the same route. 

It can even take into account other factors, like the customers’ age, location, socio-economic status and so on to offer more personalized recommendations. Maybe women aged 55-65 often go on to buy a very different supplementary item to their male counterparts, for example.

2. Image recognition

Because supervised learning algorithms are good at spotting patterns in data, they are often used to power image recognition software. If you show the algorithm a bowl of fruit, for example, and train it to recognize a curved yellow fruit as a banana, a round orange one as an orange, and so on, it will quickly learn the characteristics of each fruit and be able to identify them for itself.

This is a good example of the supervised learning algorithm leveraging its learnings from training data (in this case, the bowl of fruit) and applying that knowledge to new test data (i.e. new items of fruit that it is shown).

3. Calculating route times

Today’s mapping apps can predict how long it will take you to travel to a certain destination with a high degree of accuracy, thanks to supervised learning algorithms.

In order to provide a user with an accurate journey time, the algorithm will analyze all kinds of data, including the time of day, day of the week, distance, weather conditions, traffic, and more. It then analyzes journey data from other users doing similar journeys – or even the same one – under similar conditions to provide that particular user with an estimated time of arrival. And it all happens in just a few seconds. Something to think about next time you fire up your satnav.


Supervised learning is best suited to situations with a set of available reference points (like training data) or a ground truth (this is what a banana looks like) which can be used to train the algorithm. In these situations, it can provide highly accurate and reliable results for a wide range of real-world use cases.

* Want to learn more about how AI learns to make personalized recommendations through unsupervised learning, and how it can be applied in advertising? Download our white paper ‘From Supervised to Unsupervised Learning: How AI Is Reshaping Advertising’ for more insights. Still got a question? Contact our team today for more information!


Let us know the marketing challenges that you’re facing, and how you want to improve your marketing strategy.


Accelerate Digital Transformation With Artificial Intelligence in APAC

As customer expectations continue to intensify in an increasingly competitive environment, companies can stay ahead of the curve only by adopting the latest and best that digitalization has to offer. For those frenziedly pursuing a digital transformation agenda, artificial intelligence (AI) has the potential to positively impact the way they do business. A new commissioned study conducted by Forrester Consulting on behalf of Appier shows that AI has a critical role to play in accelerating digital transformation in Asia Pacific (APAC). Companies are well aware of the powerful insights that AI can generate. More importantly, they are testing how they can use AI to transform their business models and enhance experience across the entire customer life cycle. The June 2018 study surveyed 260 business and IT leaders across eight markets (Japan, South Korea, Singapore, Taiwan, China, India, Australia and Indonesia) who are directly responsible for technology purchasing decisions. Covering the key industries of telecom, insurance, banking, IT and retail, the study threw up some interesting insights on how companies perceive AI’s role within their businesses. Some of the key benefits that companies are expecting by using AI tools are: 1. Easily discover relevant prospects, and boost chances of conversion 2.

How AI Is Making Quality Not Quantity the Answer to Ad Effectiveness

How many times should a prospect see an ad for it to be effective? It’s a decades-old marketing dilemma – one that digital has made both easier and more difficult to manage. However, this question of effectiveness isn’t just about quantity. It’s also about quality aka finding and targeting the ideal audience – something artificial intelligence (AI) can help you do better.   Why Frequency Capping Isn’t Enough While frequency capping has certainly helped with controlling ad exposure by enabling limit setting on an individual’s contact with a campaign, the question of how many times is best continues to cause stress. Too few times and prospects may not notice it, or notice it enough; too many and there’s a chance it might move from being memorable to annoying, resulting in a poor response and, worse still, a damaged brand reputation. Plenty of research has been done over the years in an attempt to find an answer. While brands like Procter & Gamble have capped their digital ad exposure at three times a month, Facebook claims one to two impressions weekly over at least 10 weeks for a campaign would be ideal. However, searching for the right answer is kind of

Everything You Need to Know About Exit-Intent Technology

Exit-intent technology is a highly-underused tool. However, when implemented effectively, it can encourage visitors who would have otherwise abandoned your site to stay and even to convert. So, what is exit-intent technology? When should you use it? How to make it work for your brand? Read on.    What Is Exit-Intent Technology? Exit-intent technology is smart behavioral software that tracks online visitors’ mouse movements on a webpage. The purpose of exit-intent technology is to help marketers detect when a visitor is going to leave a site without making a purchase. Every exit-intent provider uses different algorithms and technology and responds to different types of visitor mouse activity. Movements that indicate abandonment include when the cursor moves towards the back button, remains idle, or scrolls up quickly. When signs of abandonment or customer intent such as these are identified, exit-intent technology automatically delivers a pop-up message that engages visitors with an enticing offer, product recommendation, or other messages to encourage them to stay and convert.   When Should Marketers Use Exit-intent Technology? Exit-intent technology works well on e-commerce websites, landing pages, and commercial websites. Marketers should use exit-intent technology if they are looking to capture more potential customers, reduce cart adornment,