What is supervised learning?
Supervised learning in this context is not about babysitting troublesome infants while their parents are away but rather in the world of artificial intelligence and machine learning, it is a process of compiling input and output data and classifying them into information which serve as a basis of learning and reference in the future.
Simply put supervised learning is a way of teaching a machine to find patterns and learn through guidance and experience through the use of input and output data
Through the use of training data, which is pre recorded and stored information that is used as a basis to generate new information, supervised learning supplies a plethora of information and algorithm with precise calculations that can determine future outputs and outcomes. These training data are useful in pairing input information with expected outputs. For example, feeding information on cars and buses to an AI over a period of time, the AI would eventually be able to properly categories both vehicles without the need of any further assistance from the training data.
Supervised learning can be very effective but it is not improvisational, as the system may not be able to handle new information that is not part of the training data.
For example, in a system that is allocated different cars and buses to sort out, once a bike is introduced, it could confuse the system and make the system categorize the bike into the closest possible option between the cars and the buses.
There are two main areas of supervised learning which are:
Classification is used to predict discrete and direct responses that are binary like for example whether a football team will win or lose a match or whether a website link is legit or fake. These terms WIN, LOSE, LEGIT or FAKE are all predefined classes which the output has to fall under depending on the input of the data. That is for each input in the football match, the output can either be a win or a loss.
Regression is used for predicting continuous and non-binary responses like weather forecasts or prediction for sports betting. By collating data from previous information and making deductions to accurately determine future results.
Supervised learning is applicable in everyday usage and take place in many related instances. For example, most modern-day medical diagnosis predominantly uses supervised learning as well as digital personal assistants like Siri and YouTube recommendations and ads matching your interest.
Supervised learning is constantly growing and evolving and getting more integrated into our daily life. By daily compiling data from past information and predicting future outcomes based on experience, it becomes easier to predict and determine outcomes that can be hugely beneficial to us.