Difference Between Supervised and Unsupervised Learning in Tabular Form: Supervised and unsupervised learning in machine learning is two very important types of learning methods. The key difference between supervised Vs unsupervised learning is the type of training data. For supervised learning, the training dataset is labeled and in unsupervised learning, the dataset is unlabeled which means no supervision is required for unsupervised learning.
Difference Between Supervised, Unsupervised and Reinforcement Learning in Tabular Form
| Factors | Supervised Learning | Unsupervised Learning | Reinforcement Learning |
|---|---|---|---|
| Data | Labeled Dataset | Unlabeled Dataset | Reinforcement learning requires no datasets, it creates data as it learns |
| Motive | Predicts the Output | To Find Hidden Pattern | To Learn from Data |
| Algorithms | Logic Regression, Neural Network, Support Vector Machines (SVMs) | K-Means, Random Forests, Hierchical Clustering | Q-Learning, SARSA |
| Examples | Classifying image as spam or no spam, voice recognition, labelling webpages | Grouping Customer according to Purcasing Behavior | Chess Game |
| Types | Regression Classification | Clustering Association | Positive Negative |
| Supervision | Required Supervision | Not Required | Not Required |
Supervised Machine Learning
From movie recommendation in Netflix to weather forecasting, we are using the features of supervised machine learning which is based on the LABELED data analysis. In the supervised machine learning, algorithms are trained with data which are labeled. In this learning process, data comes with a description, labels, targets, desired outputs, etc.
It is mostly used in a real-world application. We are using it in our everyday life, from online shopping where we are seeing the recommended and suggested buying items to the streaming movies or series where we are using the recommended movies to watch next based on our preference.
Supervised learning examples
There are plenty of services that we use daily; one of the most used services would be the auto-tagging feature in social media. The feature has freed us from the manual searching hassle as we do not need to search for specific names to get that person tags on the pictures.
I, myself find that feature helpful mostly because when I upload a group picture with several other social media friends they are getting tagged automatically. This feature is one of the real-world examples of supervised machine learning where data in forms of an image are trained with supervision and based on that the system is providing almost accurate prediction and suggestion.
The same goes for movie recommendation; plenty of data are trained with supervision from similar choices of other users based on which the system is suggesting us movies and series. And most of the time we end up watching the movie that pops up on the suggestion, making it easier to decide on what to watch next. An interesting fact, Almost 70% of Netflix users use the recommendation of movies which is based on the supervised machine learning model.
Another example would be the estimation of real estate prices. It is possible to build a system to estimate the price of the land or a house based on size, location, etc. For getting an estimation from the supervised learning system, a dataset needs to be created with a label and then teach the algorithm how various factors affect various prices. If these steps are done properly then, the algorithm will learn how to calculate prices of real estate based on the input data and would provide a valid estimated price.
Unsupervised Machine Learning
In simpler terms, unsupervised machine learning works more on an automated basis. In the unsupervised learning system, unlike the supervised model, human supervision is barely minimum. Except for some indication, it can do a lot of stuff by itself.
It deals with unlabeled datasets which means labeling the data is difficult as it is more of a complex system. Normally, it discovers hidden patterns and anomalies which is being used in work like fraud detection. Unsupervised learning is one of the most powerful tools out there for analyzing data that are too complex for a human to understand a found pattern in them.
This learning model clusters similar input in logical groups. Some of the algorithms of unsupervised machine learning are
- Self Organizing Map (SOM)
- Adaptive Resonance Theory (ART)
- K-Means
- Random Forests
- Hierarchical Clustering
Supervised vs Unsupervised Learning in Data Mining
Data mining typically has two types of problem categories.
- Directed or Supervised Learning
- Undirected or Unsupervised Learning
Directed or Supervised Learning
In supervised learning, there is typically some idea of indication. For example, the motive is to find customers who are likely to buy a gaming console. The mining data would provide a prediction based on the behavior of the data that are more likely to buy a gaming console. This model is also called a predictive model as the model predicts the behavior of the data. The predictive model supports various techniques for data mining, some of them are
- Classification technique
- Regression Technique
- Attribute Importance Technique
Undirected or Unsupervised Learning
In this method, there are a lot of large factors working which makes it difficult to extract related information. In other words, no idea about what type of pattern or relation would be found from the data mining. From a large set of data, some relation may arise which is invisible to the human eye.
An example of it could be, based on census data for the population; age, occupation and other sets of data, it may find several packets of data where the immunity of people is really strong, and possibly pointing out that the Old age population of the area is quite less and the average age of people in that are in a specific range behind the strong immunity of that small packet of population.
In this unsupervised learning model, the data often describe a structure or property and because of its descriptive nature, it is also called a descriptive model. Some of the techniques used in the unsupervised model are
- Association technique
- Clustering technique
- Feature extraction technique
Supervised vs Unsupervised learning which is better?
People often asked that very question as it often arises in our mind that which of the learning method is better?
Sorry to disappoint you a little, it cannot be said specifically about the better method between these learning models. As they both are designed for various purpose and they both yields different types of result.
Supervised learning comes with human supervision where unsupervised learning has only a little amount of human supervision, most of the time it does not require any human supervision.
In order of complexity, unsupervised learning is more complex compared to supervised learning and it’s more like actual Artificial Intelligence, performing tasks on its own. But, the use of supervised learning is wider and we are using its features way more in our daily life.
Conclusion
What is the Difference Between Supervised and Unsupervised Learning in Tabular Form? Supervised and unsupervised learning are the two most used methods of machine learning which has limitless potential. They are a part of our daily life, ones making our life a little bit easier and the other one is trying to find anomalies and hidden patterns so that serious problems like fraud detection could easily be solved.