Supervised learning is a type of machine learning algorithm that uses a known data-set (called the training data-set) to make predictions.
Category of supervised learning:
- Regression: In a regression problem, we are trying to predict results within a continuous output, meaning that we are trying to map input variables to some continuous function.
- Classification: In a classification problem, we are instead trying to predict results in a discrete output. In other words, we are trying to map input variables into discrete categories.
Given data about the size of houses on the real estate market, try to predict their price. Price as a function of size is a continuous output, so this is a regression problem.
(a) Regression – For continuous-response values. For example given a picture of a person, we have to predict their age on the basis of the given picture
(b) Classification – for categorical response values, where the data can be separated into specific “classes”. For example given a patient with a tumor, we have to predict whether the tumor is malignant or benign.
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machine-learning – Machine learning and it's classification – What is supervised learning ? - CodeDay