Explore: Decision Trees

generate such optimal trees have been devised, such as ID3/4/5, CLS, ASSISTANT, and CART. Among decision support tools, decision trees (and influence diagrams)

class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. More generally

learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and redundant to classify instances

typically simple decision trees. When a decision tree is the weak learner, the resulting algorithm is called gradient-boosted trees; it usually outperforms

measuring game complexity use decision trees: Decision complexity of a game is the number of leaf nodes in the smallest decision tree that establishes the value

An alternating decision tree (ADTree) is a machine learning method for classification. It generalizes decision trees and has connections to boosting.

In the context of decision trees in information theory and machine learning, information gain refers to the conditional expected value of the Kullback–Leibler

sequence. As a result, this also works for randomized decision trees as well. Other decision tree lower bounds do use that the query is a comparison. For

An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree methods, such as C4.5,

labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees. In decision analysis