Machine Learning Glossary

Question 1

classification model

Answer 1

A type of machine learning model for distinguishing among two or more discrete classes. For example, a natural language processing classification model could determine whether an input sentence was in French, Spanish, or Italian. Compare with regression model.

Question 2

regression model

Answer 2

A type of model that outputs continuous (typically, floating-point) values. Compare with classification models, which output discrete values, such as “day lily” or “tiger lily.”

Question 3

label

Answer 3

In supervised learning, the “answer” or “result” portion of an example.
Each example in a labeled data set consists of one or more features and a label. For instance, in a housing data set, the features might include the number of bedrooms, the number of bathrooms, and the age of the house, while the label might be the house’s price. In a spam detection dataset, the features might include the subject line, the sender, and the email message itself, while the label would probably be either “spam” or “not spam.”

Question 4

feature

Answer 4

An input variable used in making predictions.

Question 5

example

Answer 5

One row of a data set. An example contains one or more features and possibly a label.
See also labeled example and unlabeled example.

Question 6

labeled example

Answer 6

An example that contains features and a label. In supervised training, models learn from labeled examples.

Question 7

unlabeled example

Answer 7

An example that contains features but no label.

Unlabeled examples are the input to inference. In semi-supervised and unsupervised learning, unlabeled examples are used during training.