Unsupervised Learning is a type of machine learning where the models are trained on questions(input) without answers(expected output), so the model learns to answer the question on its own, if new questions were asked to model then it will give answers related to other answers.
Pros
- This type of learning is used to make clusters of questions with similar answers.
- The model can be trained by unstructured data, which means questions can be missing or incomplete.
- The model is very useful for finding similar answers.
Cons
- Most of the time models answer inaccurately.
- The model requires lots of questions to learn for giving accurate answers.
This kind of machine learning is very useful if you have enough data to train the model otherwise, the model will predict very inaccurate answers
Applications of supervised learning
- Predicting a similar search result
- Gathering similar types of questions
Approved by experts
