Advantages and Disadvantages of unsupervised Learning?

Advantages of Unsupervised Learning

There are some reasons why we sometimes choose unsupervised learning in place of supervised learning. Here are some of the advantages:

  • Labeling of data demands a lot of manual work and expenses. Unsupervised learning solves the problem by learning the data and classifying it without any labels.
  • The labels can be added after the data has been classified which is much easier.
  • It is very helpful in finding patterns in data, which are not possible to find using normal methods.
  • Dimensionality reduction can be easily accomplished using unsupervised learning.
  • This is the perfect tool for data scientists, as unsupervised learning can help to understand raw data.
  • We can also find up to what degree the data are similar. This can be accomplished with probabilistic methods.
  • This type of learning is similar to human intelligence in some way as the model learns slowly and then calculates the result.

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.