![cover image](https://wikiwandv2-19431.kxcdn.com/_next/image?url=https://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Task-guidance.png/640px-Task-guidance.png&w=640&q=50)
Supervised learning
Paradigm in machine learning / From Wikipedia, the free encyclopedia
Dear Wikiwand AI, let's keep it short by simply answering these key questions:
Can you list the top facts and stats about Supervised classification?
Summarize this article for a 10 year old
Supervised learning (SL) is a paradigm in machine learning where input objects (for example, a vector of predictor variables) and a desired output value (also known as a human-labeled supervisory signal) train a model. The training data is processed, building a function that maps new data to expected output values.[1] An optimal scenario will allow for the algorithm to correctly determine output values for unseen instances. This requires the learning algorithm to generalize from the training data to unseen situations in a "reasonable" way (see inductive bias). This statistical quality of an algorithm is measured through the so-called generalization error.
![](http://upload.wikimedia.org/wikipedia/commons/thumb/9/90/Task-guidance.png/320px-Task-guidance.png)