Terminology and notations in supervised learning

04 Feb 2018

General

The goal of supervised learning is to use a set of measures, called inputs, to predict some outputs. Inputs and outputs are sometimes also called

inputs	outputs
predictors	responses
independent variables	dependent variables
features

Categories of outputs

Outputs can be of two types,

• quantitative: outputs are numbers, with an ordering. Prediction with quantitative outputs is called regression.
• qualitative/categorical/discrete: outputs are sets, with not necessarily an order relationship. Prediction with qualitative outputs is called classification.

Notations

• Input variables are denoted by $X=\{X_j\}_j$ (vector).
• Outputs are denoted by $Y$ (quantitative) or $G$ (qualitative).
• Predicted or estimated quantities are marked with a hat.
• $X,Y,G$ are generic variables. The actual observed values are denoted in lowercase; the $i^\text{th}$ observed input value is $x_i$ (potentially a vector).

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