To describe supervised learning,we establish notation for future use.
**$x^i$ **denote the input variables also called input features
**$y^i$ ** denote the output variables also called target variables that we are trying to predict.
A pair **($x^i, y^i$) **is called a training set.
Note: i is just a superscript implying an index to the training set, and has nothing to do with exponentiation.
X denote the space of input values.
Y denote the space of output values.
If the target variables are continuous, we call the learning problem a regression problem.
If Y can take on only a small number of discrete values, we call the learning problem a regression problem.
