Supervised Learning has the goal of predicting a value (outcome) from particular characteristics (predictors) that describes some behaviour.
The attribute used to trained and being predicted is called the Target (outcome, …) attribute.
When the outcome attribute is:
- discrete, it's a classification problem
- continuous, it's a regression problem
Supervised learning is also known as directed learning. Directed data mining attempts to explain the behaviour of the target as a function of a set of independent attributes or predictors.
Supervised learning generally results in predictive models. This is in contrast to unsupervised learning where the goal is pattern detection.
The building of a supervised model involves training with a training data set of observations.
In supervised learning, you infer the general equation. For instance in linear regression, you infer the m and b of the equation y = bx+ m from the x and y
- Accurately predict unseen cases.
- Assess the quality of our predictions and inferences.