Source: https://developers.google.com/machine-learning/crash-course/framing/ml-terminology

Framing

• How to frame a task as a machine learning problem

Learning Objectives

• Refresh the fundamental machine learning terms.

• Explore various uses of machine learning.

ML Terminology

Label

• Label: is the true thing we’re predicting: $y$
  • The $y$ variable in basic linear regression

Features

• Features: are input variables describing our data: $x_i$
  • The ${x_1, x_2, … x_n}$ variables in basic linear regression

Example

• Example: is a particular instance of data, $x$
  • Labeled example: has {feature, label}: ($x, y$)
    • Used to train the model
  • Unlabeled example: has {feature, ?}: ($x$, ?)
    • Used for making predictions on new data

Model

• Model: defines the relationship between features and label.
  • Training phase: means creating or learning the model. Show the model labeled examples and enable the model to gradually learn the relationship between features and label.
  • Inference phase: means applying the trained model to unlabeled examples.

Regression model

• Regression model: predicts continuous values. For exampl,
  • What is the value of a house in California?
  • What is the probability that a user will click on this ad?

Classification model

• Classification model: predicts discrete values. For example,
  • Is a given email message spam or not spam?
  • Is this an image of a dog, a cat, or a hamster?