The advancement of artificial intelligence and machine learning continues to transform all industries through the ability of computers to analyse data and make intelligent decisions. Today’s computer applications leverage Machine Learning across many areas, including recommendation systems, identifying fraudulent transactions, classifying images, and segmenting customers.
When it comes to Machine Learning, there are two major paradigms: supervised and unsupervised learning. For those who are studying data science, artificial intelligence, or analytics, you must understand what distinguishes these two methods from one another.
In this post, we will detail how each method works, its key differences, example applications within the real world, advantages, disadvantages, and examples of when to use each method.
Understanding Machine Learning
Machine learning is a type of AI (artificial intelligence) that lets systems learn to identify and understand patterns in data by discovering these patterns on their own (not programming explicit rules). Instead of manually coding rules for the system to follow, developers can create models by training an algorithm on a dataset for the model to predict future outcomes or find patterns in data that have not yet been identified.
Machine learning algorithms are classified into three basic types of algorithms:
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
Supervised learning and unsupervised learning algorithms are the most frequently used types of algorithms from the above list within the context of the real world.
What Is Supervised Learning?
In supervised machine learning, the model learns by using data that have already been labelled (i.e., each piece of data has its corresponding output labelled with the correct answer). The model will then use what it learns about the relationship of the input variables’ values to the output label’s value to be able to predict an output for new/unknown data at a later time.
In simpler terms, supervised machine learning looks at an example, with the corresponding correct answer included in the training set, and learns from it.
To illustrate, consider the case where we want to predict the price of a house. Our house dataset may contain the following features:
- House size
- Location
- Number of bedrooms and bathrooms
- House price (labels)
Once the model learns how these features correlate with the house price, the model will use that information to predict the price of a house that it has never seen.
Types of Supervised Learning
The two types of supervised learning can be divided into these broad categories –
Classification – Classification is used to predict data into categories, and as a result, is useful for predicting which category is associated with an observing input.
Some example applications include
- Email Spam Detection
- Disease Diagnosis
- Image Recognition
- Sentiment Analysis.
Therefore, the observed outcomes from classification problems are discrete.
Regression – Regression is employed to predict the continuous value of independent input variables, resulting in a numeric value.
Some example applications include
- Sales Estimates
- Temperature Estimates
- Stock Prices
- Estimated Values
- House Price Estimates.
Therefore, the output from regression problems is numeric.
How Supervised Learning Works
The overall process used for supervised learning is as follows
- Collect Labelled Data
- Split Data into Training and Testing Sets
- Train the Model Using the Training Set
- Evaluate the Model Performance
- Make Predictions Based on New Inputs
The model will improve as more examples are used in the training process.
Benefits of Supervised Learning
There are many benefits to using supervised learning.
One is that there is a greater chance of having an accurate model due to having enough labelled data to train on.
Two, it is easy to evaluate how well your model performs by using accuracy and other evaluation metrics (precision, recall, error rate).
Three, many businesses will find it very predictable and reliable for solving many business problems as well.
Fourth, some industries already have large labelled datasets, so it should be fairly straightforward to implement supervised learning in these industries.
Drawbacks of Supervised Learning
However, despite these benefits, there are a few issues within supervised learning.
One, it is difficult to obtain labelled data from the market, as it can take a long time and be quite costly to obtain.
Two, overfitting of the model is another challenge because the model could perform well on the training data but may not generalise well when tested on new data. Therefore, there is potential for the supervised learning model to not work properly on new data if an unexpected pattern not encountered during training is encountered when validating against the new data.
Utilizing Supervised Learning – Practical Applications of the Real World
Given that numerous industries use supervised learning, it has proven to be very beneficial across all sectors.
For instance, in the healthcare domain, supervised learning algorithms can predict potential diseases based on an individual’s historical medical data.
In finance, supervised learning can identify fraudulent transactions.
With respect to marketing applications, supervised learning can forecast potential customer behaviours.
With e-commerce platforms, recommendation engines are powered by supervised learning and help customers choose products.
In the case of manufacturing, supervised learning can warn of potential machine failure based on prior equipment failure data.
Because supervised learning targets prediction, it is an excellent solution for providing decision support.
What is Unsupervised Learning?
When an algorithm is trained on unlabeled data to find hidden structure or patterns within the data without predetermined outputs, this is commonly referred to as “unsupervised learning”.
Unlike supervised learning, whereby an algorithm predicts an answer, unsupervised learning examines the data, trying to identify any possible relationships.
As an example, if a company wishes to group customers by their buying patterns, the company may not know beforehand what the groups will be. By using unsupervised learning, the algorithm examines prior customer purchases and automatically creates the groups.
In general, unsupervised learning can be used for data exploration purposes.
Key Differences Between Supervised and Unsupervised Learning
The main characteristics that distinguish supervised from unsupervised learning are related to data labelling and purpose.
Predictive modelling is one aspect of supervised machine learning; the two major types include the elimination of labels from datasets and the discovery of patterns in unlabelled data. A key difference is that supervised methods look at prediction accuracy as their measure of success, while exploratory data analysis using unsupervised methods examines how similar and different all observations in a dataset are.
Another characteristic is that supervised machine learning problems can be more easily evaluated than project requirements for unsupervised machine learning, which has no clearly defined outcome or direction, making it difficult to evaluate.
Machine learning encompasses both supervised and unsupervised learning.
Utilizing Supervised Learning
Supervised learning is effective when:
- Data is labeled
- You want predictions
- You need measurable accuracy
- The problem is clearly defined.
Common uses for supervised learning include detecting fraud, forecasting, and building classification systems.
Utilizing Unsupervised Learning
Unsupervised learning is effective when:
- Data is unlabelled
- You have an interest in exploring patterns
- There is segmentation required
- You are working with very large data sets.
Common uses for unsupervised learning include clustering customers and/or detecting anomalies.
Combining Supervised and Unsupervised Learning
Combining different methods is often done when working with actual projects.
As an illustration, one could apply an unsupervised algorithm to classify groups of people based on their buying patterns, and then apply a supervised method to help model what these people might do in the future.
The use of both approaches results in higher performance and additional information from the model.
Data Science is seeing an increase in the utilization of hybrid models.
Data Preparation Requirements
Supervised learning involves creating a model out of clean and structured labelled datasets, meaning that any dataset needs to be cleaned and organised into its proper place with annotations before it can be trained by the model. The process of getting the dataset ready for training can take a lot of resources and time. In comparison to supervised learning, unsupervised learning uses unlabeled data. In this way, you do not have to label each record manually. However, you will still need to clean the data and select features in order to accurately discover patterns in the data.
Model Complexity and Flexibility
Typically, supervised learning models are tailored to address defined prediction challenges, which means their focus is solely concentrated on tasks. In contrast, unsupervised learning models provide greater latitude in examining the unknown from many angles. Unsupervised learning models have a higher tolerance for the variability of data than supervised learning models do, and they may also assist researchers, innovators, and analysts in discovering unanticipated relationships based on the analysis of the data.
The Future of Machine Learning Approaches
As the amount of information is increasing quickly, machine learning in all its forms will be growing increasingly valuable.
The growing amount of available information and the large amounts of unlabelled datasets being collected mean that unsupervised learning is becoming increasingly important as an area of study.
Simultaneously, the growing power of machine learning, associated with a predictive system, is being used to automate and facilitate decision-making.
With advances in computing capabilities, cloud technologies, and research involving artificial intelligence (AI) will continue to advance both methods’ abilities.
For those professionals in the data profession, understanding these resources is critical for their future success.
Conclusion
The main techniques used in machine learning are called supervised and unsupervised learning. While supervised learning focuses on prediction, using labelled data as well as discovering relationships between input variables, and predicts the output variable from a model of the input data, unsupervised learning involves finding hidden structures (or patterns) in unlabelled data.
Both methods solve different problems, but they can be combined to form complete data analysis workflows today.
Supervised learning is good for forecasting and classifying data, and unsupervised learning is commonly used for exploring new data to identify hidden information.
As machine learning continues to shape technology and business, understanding the fundamentals of both supervised and unsupervised learning is critical for everyone involved in developing technologies and systems using machine learning.