Supervised Learning is the process of making an algorithm to learn to map an input to a particular output. This is achieved using the labelled datasets that you have collected. If the mapping is correct, the algorithm has successfully learned. Else, you make the necessary changes to the algorithm so that it can learn correctly. Supervised Learning algorithms can help make predictions for new unseen data that we obtain later in the future.
This is similar to a teacher-student scenario. There is a teacher who guides the student to learn from books and other materials. The student is then tested and if correct, the student passes. Else, the teacher tunes the student and makes the student learn from the mistakes that he or she had made in the past. That is the basic principle of Supervised Learning.
Types of Supervised Learning
Supervised Learning has been broadly classified into 2 types.
Regression is the kind of Supervised Learning that learns from the Labelled Datasets and is then able to predict a continuous-valued output for the new data given to the algorithm. It is used whenever the output required is a number such as money or height etc.
Classification, on the other hand, is the kind of learning where the algorithm needs to map the new data that is obtained to any one of the 2 classes that we have in our dataset. The classes need to be mapped to either 1 or 0 which in real-life translated to ‘Yes’ or ‘No’, ‘Rains’ or ‘Does Not Rain’ and so forth. The output will be either one of the classes and not a number as it was in Regression.
#SupervisedLearning #MachineLearning #AI #ProbytoAI
Subscribe and follow us for latest news in Data Science, Machine learning, technology and stay updated!