Framing: Key ML Terminology Flashcards
Explore fundamental machine learning terminology.
What is (supervised) machine learning?
ML systems learn how to combine input to produce useful predictions on never-before-seen data.
What is a Label?
A label is the thing we’re predicting — the y variable in simple linear regression.
The label could be the future price of wheat, the kind of animal shown in a picture, the meaning of an audio clip, or just about anything.
What is a Feature?
A feature is an input variable—the x variable in simple linear regression.
A simple machine learning project might use a single feature, while a more sophisticated machine learning project could use millions of features, specified as:
What is an Example?
An example is a particular instance of data, x.
(We put x in boldface to indicate that it is a vector.)
We break examples into two categories:
labeled examples
unlabeled examples
Describe a Labeled Example.
A Labeled Example includes both feature(s) and the label.
That is: labeled examples: {features, label}: (x, y)
Use labeled examples to train the model.
Describe an unlabeled example.
An Unlabeled Example contains features but not the label.
That is: unlabeled examples: {features, ?}: (x, ?)
What is a Model?
A model defines the relationship between features and label.
For example, a spam detection model might associate certain features strongly with “spam”. Let’s highlight two phases of a model’s life:
Training means creating or learning the model. That is, you show the model labeled examples and enable the model to gradually learn the relationships between features and label.
Inference means applying the trained model to unlabeled examples. That is, you use the trained model to make useful predictions (y’). For example, during inference, you can predict aValue for new unlabeled examples.
What is a Regression Model?
A Regression Model predicts continuous values.
For example, regression models make predictions that answer questions like the following:
What is the value of a house in California?
What is the probability that a user will click on this ad?
What is a Classification Model?
A Classification Model predicts discrete values.
For example, classification models make predictions that answer questions like the following:
Is a given email message spam or not spam?
Is this an image of a dog, a cat, or a hamster?
What is Linear Regression
Linear regression is a method for finding the straight line or hyper plane that best fits a set of points. This module explores linear regression intuitively before laying the groundwork for a machine learning approach to linear regression.
