Applications And Drawbacks Of Unsupervised Learning

Applications And Drawbacks Of Unsupervised Learning

Unsupervised Learning is a type of machine learning in which you do not have to supervise your model. A model itself will discover all information using unlabeled data. In this technique you allow a model to work on its own. Its algorithms are more complex than supervised learning.  

Practical Example of Unsupervised Machine Learning:

Let’s take an example of a baby and a dog. A baby is used to see a photo of dogs in various books. A few weeks later her family brings a dog in house. Baby has never seen a dog before but she can identify likewise four legs two big eyes. Thus she will use all the information and she will recognize new animal as a dog.

Applications of Unsupervised Learning

• Cluster Analysis: Clustering automatically split whole data sheet into different segments according to their similarities. Unsupervised Learning provides you some features which will help you to categorize your data. One major drawback for clustering is it will treat all the data in a group sometimes it doesn’t treat data as individual.

• Unusual Data Detection: It will automatically find unusual data from your data-set. It will definitely helpful to find out if there is any fraud or not. If there is any manual mistake has done during data entry it is also going to pinpoint that. And also it can find a faulty peace or hardware in a automate process.

• Association: It identifies data set which mostly occurs together in your spread sheets. It will analyze your data and find out which things are mostly purchased together and according to that it will show the results. This will increase your productivity and accuracy.

• It will find all unknown pattern in data. Your computer having more unlabeled data than labeled data so unsupervised learning will help you to categorize them.

• Latent Variable models: These models are used for data processing for reducing the number of features in data-set or decomposing your data-set in variable components.

Drawback of Unsupervised Learning

• You cannot get precise information about sorted data-sets as a output of Unsupervised Learning is labeled and not known.

• Accuracy of output is very less because data used by Unsupervised Learning is unlabeled so system needs to do it by itself. It results reduction in output accuracy.

• Spectral class does not always in line with informational class. Spectral Properties of class can change over with the time so you don’t have always same class information as you are moving to one image to another.