Unsupervised Machine Learning

Hello Learners !! I am Honey Darji. Today in this Blog we understand what is unsupervised learning ? How we use this unsupervised machine learning and see the example of this.

Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.

Unsupervised learning is where you only have input data (X) and no corresponding output variables.

The goal for unsupervised learning is to model the underlying structure or distribution in the data in order to learn more about the data.

These are called unsupervised learning because unlike supervised learning above there is no correct answers and there is no teacher. Algorithms are left to their own devises to discover and present the interesting structure in the data.

Unsupervised learning problems can be further grouped into clustering and association problems.

• Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
• Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.

Advantages of Unsupervised Learning

• It does not require a training data to be labelled.
• The automatic labelling of the training data set saving the time spent in hand classification.
• Classification task is fast.

Disadvantages of Unsupervised Learning

• You cannot get precise information regarding data sorting, and the output as data used in unsupervised learning is labeled and not known
• Less accuracy of the results is because the input data is not known and not labeled by people in advance. This means that the machine requires to do this itself.
• The spectral classes do not always correspond to informational classes.
• The user needs to spend time interpreting and label the classes which follow that classification.
• Spectral properties of classes can also change over time so you can’t have the same class information while moving from one image to another.