Advantages and Disadvantages of unsupervised Learning?

Data Science, AI and ML
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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.