Page Summary
-
Decision forests are models composed of multiple decision trees that work together to make predictions.
-
Decision trees use conditions to split data and make decisions, with leaves representing the final predictions.
-
Various techniques like bagging, attribute sampling, and gradient boosting are used to improve the accuracy and robustness of decision forests.
-
Feature importances reveal which input features are most influential in a decision forest's predictions.
-
Ensembles, including random forests and gradient boosted trees, leverage the wisdom of the crowd for enhanced performance.
This page contains Decision Forests glossary terms. For all glossary terms, click here.
A
attribute sampling
A tactic for training a decision forest in which each decision tree considers only a random subset of possible features when learning the condition. Generally, a different subset of features is sampled for each node. In contrast, when training a decision tree without attribute sampling, all possible features are considered for each node.
axis-aligned condition
In a decision tree, a condition
that involves only a single feature. For example, if area
is a feature, then the following is an axis-aligned condition:
area > 200
Contrast with oblique condition.
B
bagging
A method to train an ensemble where each constituent model trains on a random subset of training examples sampled with replacement. For example, a random forest is a collection of decision trees trained with bagging.
The term bagging is short for bootstrap aggregating.
See Random forests in the Decision Forests course for more information.
binary condition
In a decision tree, a condition that has only two possible outcomes, typically yes or no. For example, the following is a binary condition:
temperature >= 100
Contrast with non-binary condition.
See Types of conditions in the Decision Forests course for more information.
C
condition
In a decision tree, any node that performs a test. For example, the following decision tree contains two conditions:
A condition is also called a split or a test.
Contrast condition with leaf.
See also:
See Types of conditions in the Decision Forests course for more information.
D
decision forest
A model created from multiple decision trees. A decision forest makes a prediction by aggregating the predictions of its decision trees. Popular types of decision forests include random forests and gradient boosted trees.
See the Decision Forests section in the Decision Forests course for more information.
decision tree
A supervised learning model composed of a set of conditions and leaves organized hierarchically. For example, the following is a decision tree:
