Introduction to Supervised vs Unsupervised Learning
Supervised learning and Unsupervised learning are machine learning tasks.
Supervised learning is simply a process of learning algorithms from the training dataset. Supervised learning is where you have input variables and an output variable, and you use an algorithm to learn the mapping function from the input to the output. The aim is to approximate the mapping function so that we can predict the output variables for that data when we have new input data.
Unsupervised learning is modeling the underlying or hidden structure or distribution in the data in order to learn more about the data. Unsupervised learning is where you only have input data and no corresponding output variables.
Training dataset: A set of examples used for learning where the target value is known.
Head-to-Head Comparison Between Supervised and Unsupervised Learning (Infographics)
Below are the top 7 comparisons between Supervised Learning and Unsupervised Learning:
Key Differences Between Supervised and Unsupervised Learning
Below are the lists of points that describe the key differences between Supervised Learning and Unsupervised Learning
1. Machine learning algorithms discover patterns in big data. These different algorithms can be classified into two categories based on the way they “learn” about data to make predictions. Those are supervised and unsupervised learning.
2. In supervised learning scientist acts as a guide to teach the algorithm what conclusions or predictions it should come up with. In unsupervised learning, there is no correct answer, there is no teacher, and algorithms are left to their own to discover and present the interesting hidden structure in the data.
3. Supervised learning model will use the training data to learn a link between the input and the outputs.
4. Unsupervised learning does not use output data. In unsupervised learning, there won’t ‘be any labeled prior knowledge, whereas in supervised learning will have access to the labels and will have prior knowledge about the datasets
5. Supervised learning: The idea is that training can be generalized and that the model can be used on new data with some accuracy.
6. Supervised learning algorithms: Support vector machine, Linear and logistics regression, Neural network, Classification trees, and random forest, etc.
7. Unsupervised algorithms can be split into different categories: Cluster algorithms, K-means, Hierarchical clustering, Dimensionally reduction algorithms, Anomaly detections, etc.
8. Classification and regression area widely used algorithms in supervised learning. Support Vector Machines (SVM) are supervised machine learning models with associated learning algorithms that can be used for both classification and regression purposes but are mostly used for classification problems.
9. In the SVM model, we plot each data item as a point in n-dimensional space (where n is the features we have), with the values of each feature being the value of a particular coordinate. Then the classification is performed by finding the hyperplane that differentiates the two classes.
10. The main goal of regression algorithms is to predict the discrete or continuous value. In some cases, the predicted value can be used to identify the linear relationship between the attributes. Based on the problem, different regression algorithms can be used. Some of the basic regression algorithms are linear regression, polynomial regression, etc.
11. Clustering is widely used in unsupervised learning. Clustering is the task of dividing the data points into a number of groups such that the same trait points will be together in the form of a cluster. There are many clustering algorithms; a few of them are Connectivity models, centroid models, Distribution models, and Density models.
12. Hierarchical clustering comes under unsupervised learning. Hierarchical clustering, as the name suggests, is an algorithm that builds a hierarchy of clusters. This algorithm starts with all the data points assigned to a cluster of their own. Then two nearest clusters are merged into the same cluster. In the end, this algorithm terminates when there is only a single cluster left.
13.KMeans comes under the unsupervised clustering method. Data will be partitioned into k clusters based on their features. Each cluster is represented by its centroid, defined as the center of the points in the cluster. KMeans is simple and fast, but it doesn’t yield to the same result with each run.
14. To understand supervised learning and unsupervised learning better, let’s take real-life examples. Supervised learning: Let’s take one of Gmail’s functionality as an example: spam mail. Based on past information about spam emails, filtering a new incoming email into the Inbox or Junk folder. In this scenario, Gmail is modeled as a mapping function to segregate the incoming mail based on prior knowledge about the mail; this is supervised learning.
15. Unsupervised learning: Let’s assume a friend invites you to her party, where you meet new people. Now you will classify them using no prior knowledge (Unsupervised learning), and this classification could be on any trait. It could be age group, gender, dress, educational qualification, or whatever way you would like. Since you didn’t use any prior knowledge about people and classified them, it comes under unsupervised learning.
Supervised Learning and Unsupervised Learning Comparison Table
Following are the lists of points that describe the comparisons Between Supervised Learning and Unsupervised Learning:
| Basis For Comparisons | Supervised Learning | Unsupervised Learning |
|
Method |
Input variables and output variables will be given. | Only input data will be given |
|
Goal |
The supervised learning goal is to determine the function so well that it can predict the output when a new input data set is given. | The unsupervised learning goal is to model the hidden patterns or underlying structures in the given input data in order to learn about the data. |
|
Class |
Machine learning Problems, Data Mining, and Neural Networks, | Machine Learning, Data Mining, Problems and Neural Network |
|
Examples |
|
|
| Who uses | Data scientists | Data scientists |
|
Eco-systems |
Big data Processing, Data mining, etc |
Big data Processing, Data mining, etc |
|
Uses |
Supervised learning is often used for export systems in image recognition, speech recognition, forecasting, financial analysis and training neural networks and decision trees, etc |
Unsupervised learning algorithms are used to pre-process the data during exploratory analysis or to pre-train supervised learning algorithms. |
Conclusion
Choosing to use either a supervised or unsupervised machine learning algorithm typically depends on factors related to the structure and volume of your data and the use case. In reality, most of the time, data scientists use both Supervised Learnings vs Unsupervised Learning approaches together to solve the use case.
Recommended Articles
This has been a guide to Supervised Learning vs Unsupervised Learning. Here we have discussed Supervised Learning vs Unsupervised Learning head-to-head comparison, key differences along with infographics and comparison table. You may also look at the following articles to learn more –
- Supervised Learning vs Reinforcement Learning
- Data Science vs Machine Learning
- Map Reduce vs Yarn
- What is Reinforcement Learning
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