Classification or Regression?
As you know, Supervised Learning, which is one of the main topics of Machine Learning, is divided into 2 main titles.
Classification and Regression.
So, how do we know what approach to solve the problem we have?
Here’s what we need to know:
Classification and Regression are not related to input data. It is related to the expected output.
For example, you have a picture. If you want gender information from these pictures, this is called Classification. But if you want gender knowledge in %, this is called Regression.
So as a result, if you want to classify the homes in the Y district as 100K, 200K, 300K houses according to X data, Classification is the name of the Regression if you want to estimate the values of houses with the same X data at a certain interval.
For example, the weather forecast is Regression. Why? Because tomorrow’s weather forecast can be known by a certain percentage. But if you want to separate the regions in the country such that it will be sunny, rainy, this is called Classification.
