Framing

This module investigates how to frame a task as a machine learning problem, and covers many of the basic vocabulary terms shared across a wide range of machine learning (ML) methods.

Framing

ML systems learn

how to combine input

to produce useful predictions

on never-before-seen data

  • Label is the variable we're predicting
    • Typically represented by the variable y
  • Label is the variable we're predicting
    • Typically represented by the variable y
  • Features are input variables describing our data
    • Typically represented by the variables {x1, x2, ..., xn}
  • Example is a particular instance of data, x
  • Labeled example has {features, label}: (x, y)
    • Used to train the model
  • Unlabeled example has {features, ?}: (x, ?)
    • Used for making predictions on new data
  • Example is a particular instance of data, x
  • Labeled example has {features, label}: (x, y)
    • Used to train the model
  • Unlabeled example has {features, ?}: (x, ?)
    • Used for making predictions on new data
  • Model maps examples to predicted labels: y'
    • Defined by internal parameters, which are learned
Previous
Introduction to ML (3 min)
Next
Key ML Terminology