Supervised Learning vs Unsupervised Learning

Introduction to Supervised Learning vs Unsupervised Learning

Supervised learning and Unsupervised learning are machine learning tasks.
Supervised learning is simply a process of learning algorithm 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 when we have new input data we can predict the output variables for that 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 Learning and Unsupervised Learning (Infographics)

Below are the top 7 comparisons between Supervised Learning and Unsupervised Learning:

Key Differences Between Supervised Learning and Unsupervised Learning

Below are the lists of points, 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, 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, their 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, those can be used for both classification and regression purposes, but mostly used for classification problems.

9. In SVM model, we plot each data item as a point in n-dimensional space, (where n is features we have) with the values of each feature being the value of a particular coordinate. Then the classification performed by finding the hyperplane that differentiates the two classes.

10. The main goal of regression algorithms is to predict the discrete or continues value. In some cases, the predicted value can be used to identify the linear relationship between the attributes. Based on the problem difference 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 number of groups such that same traits points will be together in the form of cluster. There are more many clustering algorithms; 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 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 unsupervised clustering method. Data will be partitioned into k clusters, based on their features. Each cluster is represented by its centroid, defined as the centre 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 the 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, which is a spam mail. Based on past information about spam emails, filtering out a new incoming email into Inbox folder or Junk folder. In this scenario Gmail is modeled a mapping function to segregate the incoming mail based on the prior knowledge about the mails, 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, dressing, 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, describe the comparisons Between Supervised Learning and Unsupervised Learning:

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 times, data scientists use both  Supervised Learning vs Unsupervised Learning approaches together to solve the use case.

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