About

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

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Objectives

• Accurately predict unseen cases.
• Understand which inputs affect the outcome. Understanding how the inputs of a system drive the outputs.
• Assess the quality of our predictions and inferences.