It is easy to get confused when comparing supervised vs unsupervised learning since both of them are essentially two techniques of machine learning. In other words, supervised and unsupervised learning describes two different ways in which machine learning algorithms can learn from data and make predictions. The most fundamental difference between them is that supervised learning algorithms already know the output, while unsupervised algorithms don’t. Let’s dig a little deeper to understand the differences and core functionalities of these two.
Supervised vs Unsupervised Learning: What is the difference?
Before we go into the differences between supervised and unsupervised learning, let’s discuss what these terms actually mean:
In supervised machine learning, you train the machine with the help of labeled data in order to predict outcomes of unforeseen data. By analyzing both desired inputs and outputs, the algorithm has to determine the right method to arrive at those particular outputs.
While we already know the answer to the problem, the algorithm’s responsibility is to recognize the data patterns involved and make accurate predictions about unavailable, unseen, and future data based on those patterns. In this case, the programmer is responsible for correcting the algorithm in order to achieve a high accuracy level.
Supervised learning algorithms can be divided into two main categories:
The algorithm is tasked with determining which category the given data belongs to, based on the previous values or data. If the algorithm tries to label the input data into two distinct categories, then it is called binary classification. In case there are more than two categories, then it is referred to as multiclass classification.
An example of classification in supervised learning is determining whether a customer is likely to default in paying their loan or not. Another example is email spam detection where the algorithm has to determine whether the email is spam or not.
In regression unsupervised learning, the algorithm needs to determine a real or continuous output, like age or weight of a person. It can also be used to predict future stock prices of a company. An example of the regression algorithm is using an equipment’s performance history as input to determine when the next malfunction will occur and schedule maintenance accordingly.
In unsupervised learning, the machine does not need any supervision or training of any kind. The algorithm is responsible for learning on its own by determining and adapting according to the characteristics of the input data. It also uses unlabelled data to detect patterns, identify information structure, and discover valuable insights on its own. While it allows you to perform more complex processes, as compared to supervised learning, it is not as accurate as its counterpart.
The main goal of unsupervised learning is to analyze and identify the innate structure of the dataset.
The two main categories of unsupervised machine learning include:
Clustering unsupervised algorithms are mainly used to categorize input data into different clusters or groups based on the pattern of the data. Since there are no previously known groups, the algorithm has to first segment data according to the similarities and dissimilarities and then divide the data into different categories. For instance, clustering can be used in manufacturing to detect any anomalies in production equipment and find the root cause behind the malfunctions.
These algorithms are mainly used to discover relationships in the distribution of the input data. Association algorithms can be used for determining consumer behavior and target users accordingly for maximum conversions.
Supervised vs Unsupervised Learning: Key Differences
Let’s understand what are the key differences between supervised and unsupervised learning.
1- Supervisvised vs Unsupervised Learning: Data available
Supervised learning: Both input and output data is available
Unsupervised learning: Only unlabeled input data is available
2- Supervised vs Unsupervised Learning: Goal
Supervised learning: The main goal is to understand the relationship between input and output data, and predict future data accordingly.
Unsupervised learning: The main goal is to identify the underlying structure and hidden pattern present in the input data.
3- Supervised vs Unsupervised Learning: Feedback
Supervised learning: It takes direct feedback from the programmer to check if the predictions are correct or not.
Unsupervised learning: It does not take any feedback.
4- Supervised vs Unsupervised Learning: Complexity and accuracy
Supervised learning: While it is comparatively less complex, it provides a higher accuracy rate:
Unsupervised learning: It more complex than supervised learning and the accuracy levels are also relatively less
5- Supervised vs Unsupervised Learning: Use cases
Supervised learning: It is often used for speech recognition, image recognition, financial analysis, forecasting, and training neural networks.
Unsupervised learning: It is mainly used to pre-process data or to pre-train supervised learning algorithms.
Adopting, learning, and executing machine learning starts by understanding the key differences between supervised vs unsupervised learning. Springboard offers a dedicated machine learning course that can guarantee make you an expert machine learning algorithm in just 6 months.