Machine Learning

Sections

Labels are variables that being predicted

Mathematically it is represented by the variable $y$

E.g. An email can be sorted at spam or not spam

Features are variables that describe the data

Mathematically it is represented by the variable ${x_1,x_2,...,x_n}$

E.g. A feature could be the to and from address or words inside the body

Examples are a particular instance of data, $x$

There are two types of examples:

Labeled examples are used to train the model

It has a feature and a label $(x,y)$

Unlabeled examples are used to make predictions on new data

It has features but there isn't a label attached to it $(x,?)$

Models maps examples to predict labels $y'$

They are defined by internal parameters, which are learned