Supervised learning is like learn a path already known, and use the knowledge for new paths. If you have data with correct outputs, It can be used for train a model, and the model can be used for predict to answers.
There are two main part of supervised learning.
Classification
It is like putting fruits that is same type to same pack. It separate a data to parts. The number of parts should be limited. The number of parts should not be continuous. In that kind of situations, we can use classification.
Regression
If you do not have limited chose, you have a regression problem. Basic example about it is house price prediction. House prices are continuous, so we should use regression algorithms.
Note: Logistic regression is a classification algorithm. It is kind a classifying a regression output.
Thank you for reading. I hope The writing is clear and short, Let me know.
