Skip to main content

GLOSSARY


Browse the glossary using this index

Special | A | B | C | D | E | F | G | H | I | J | K | L | M | N | O | P | Q | R | S | T | U | V | W | X | Y | Z | ALL

B

Bagging or Bootstrap aggregating

Type of ensemble that uses a different subset of the training data, in this case, M estimators are trained independently.

Binary classification

A supervised learning problem where the output variable has only two possible states.

Bioinformatics

An interdisciplinary field dedicated to the development of methods and software for understanding biological data. Bioinformatics combines biology, computer science, engineering, mathematics, and statistics to analyze and interpret biological data.

Black box model

A type of machine learning model whose representation is complex, not directly understandable or readable by the user, and where the inference procedure used to determine the output is unknown.

Boosting

Type of ensemble that uses weights or costs for examples that are more difficult to identify correctly. In this case, M iterations are performed, with each one generating an estimator dependent on the result of the previous stage.