What is the difference between supervised and unsupervised learning? In making artificial intelligence or AI using machine learning, there are two approaches; supervised and unsupervised learning.
Both can produce accurate AI. However, the way it works is quite different. Consider the following explanation to determine the difference between supervised and unsupervised learning.
Supervised and Unsupervised Learning
Machine Learning models can be distinguished depending on their usage — Supervised and Unsupervised. They are terms used to separate models in certain functions. In short, we can say that Supervised Learning is a machine learning model that requires target data.
In contrast, Unsupervised Learning does not require target data. Even so, is it only there that the difference? In fact, the difference goes deeper than that. Let’s review them one by one below.
Intro to Supervised Learning
Supervised learning is an approach to creating AI. It is called “supervised” because, in this approach, machine learning is trained to recognize patterns between input data and output labels.
Not only that but machine learning is also trained to identify the relationship underlying the input data connection with the output label.
If compared, supervised learning is learning at school. When studying at school, you study material or subjects that have been determined. Likewise managed to learn, machine learning is trained to analyse data that has been specially labelled.
Intro to Unsupervised Learning
Unsupervised learning is a technique used by machine learning in making artificial intelligence. In this approach, you don’t need to train a computer algorithm to recognize the AI’s constituent patterns.
The model is designed to be “self-learning” in gathering information, including identifying unlabeled data. It is called “unsupervised” because the model in this approach does not need to be trained.
Unsupervised learning algorithms are usually used in more complex and complicated procedures.
Supervised and Unsupervised Differences
Then, what exactly is the difference between supervised and unsupervised learning? The most basic difference lies in how the computer algorithm works in each approach.
In supervised learning, the algorithm is trained before it can work. Meanwhile, unsupervised learning computer algorithms have been designed to work immediately, even without being introduced first.
To make it easier for you, here are some points that differentiate supervised and unsupervised learning:
1. Uses
When viewed from their usefulness, these two approaches are very different. Supervised learning will help you to collect or produce output data from past experiences.
It is similar to human memory. You can only remember someone’s name when you’ve met or met before.
Meanwhile, unsupervised learning is often used to find all unknown patterns in the data. An example of an application often used in everyday life is time prediction on a digital map.
The system will include a predicted travel time when using a digital map to find a route to a certain location. In fact, you’ve never driven to that location.
2. Working Process
You will get input and output data variables in the supervised learning model. However, this does not occur in unsupervised learning. In that approach, you will only get input data.
This is because supervised learning allows you to collect or produce output data from previous experiences. Meanwhile, unsupervised learning is not designed to “learn” from earlier experience, so it does not have data output.
3. Learning Process
As you already know, machine learning does learning to be able to recognize data. However, the learning process for each machine-learning approach is different. Some do it in real-time, but some do it offline or prepare study materials before dealing directly with data.
In supervised learning, computer algorithms carry out learning offline before encountering data. That is, the computer is “equipped” with a certain amount of material so it can later recognize data easily.
This does not happen in unsupervised learning. In this approach, computer algorithms study data in real time. That means when the computer is dealing with data, at that very moment, the computer learns to recognize data.
4. Concept
Conceptually Supervised Learning is a Machine Learning model that studies data with labels or targets where the evaluation of the model will be based on this target. On the other hand, Unsupervised Learning is a Machine Learning Model that studies data patterns without any target data.
5. Model
The models in Supervised Learning require training data in the form of input data and the desired target data. This model is trained to make predictions based on patterns found in responding to target data.
Whereas Unsupervised Learning only requires input data without examples of target data. This model tries to find patterns and important insights from the data. Often, this process is also known as data mining.
6. Training Data
Supervised Learning uses training data to create machine-learning models. These models will be used to test on test data. Unsupervised Learning does not use training data and only depends on test data, so we cannot evaluate the model.
7. Algorithm
Examples of model algorithms from Supervised Learning are classification algorithms for predicting categorical features (Yes/No, Like/No, etc.) and Regression for predicting continuous features (house prices, stock prices, etc.). For Unsupervised Learning, Clustering to perform data segmentation (customer segmentation, risk segmentation, etc.) and Dimensional Reduction.
Examples of models that are often applied are:
- Supervised Learning: Linear Regression, Logistic Regression,
- Random Forest, XGBoost, K-NN, SVM
- Unsupervised Learning: K-Means, DBSCAN, PCA, SVD
Conceptually Supervised Learning is a Machine Learning model that studies data with labels or targets where the evaluation of the model will be based on this target.
On the other hand, Unsupervised Learning is a Machine Learning Model that studies data patterns without any target data.
8. Model
The models in Supervised Learning require training data in the form of input data and the desired target data. This model is trained to make predictions based on patterns found in responding to target data.
Whereas Unsupervised Learning only requires input data without examples of target data. This model tries to find patterns and important insights from the data. Often, this process is also known as data mining.
9. Training Data
Supervised Learning uses training data to create machine-learning models. These models will be used to test on test data. Unsupervised Learning does not use training data and only depends on test data, so we cannot evaluate the model.
10. Algorithm
Examples of model algorithms from Supervised Learning are classification algorithms for predicting categorical features (Yes/No, Like/No, etc.) and Regression for predicting continuous features (house prices, stock prices, etc.).
For Unsupervised Learning, Clustering to perform data segmentation (customer segmentation, risk segmentation, etc.) and Dimensional Reduction.
Examples of models that are often applied are:
- Supervised Learning: Linear Regression, Logistic Regression,
- Random Forest, XGBoost, K-NN, SVM
- Unsupervised Learning: K-Means, DBSCAN, PCA, SVD