Decision tree learning is a supervised learning approach used in statistics, data mining and machine learning. In this formalism, a classification or...

A decision tree is a decision support recursive partitioning structure that uses a tree-like model of decisions and their possible consequences, including...

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

decision forests is an ensemble learning method for classification, regression and other tasks that works by creating a multitude of decision trees during...

compression technique in machine learning and search algorithms that reduces the size of decision trees by removing sections of the tree that are non-critical and...

successful applications of deep learning are computer vision and speech recognition. Decision tree learning uses a decision tree as a predictive model to go...

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

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

two types: Multi-relational decision tree learning (MRDTL) uses a supervised algorithm that is similar to a decision tree. Deep Feature Synthesis uses...

In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision tree from a dataset. ID3...

Instance-based learning Lazy learning Learning Automata Learning Vector Quantization Logistic Model Tree Minimum message length (decision trees, decision graphs...

corresponding learning algorithm. For example, one may choose to use support-vector machines or decision trees. Complete the design. Run the learning algorithm...

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

Version Space, Valiant's PAC learning, Quinlan's ID3 decision-tree learning, case-based learning, and inductive logic programming to learn relations....

reduces variance and overfitting. Although it is usually applied to decision tree methods, it can be used with any type of method. Bagging is a special...

learning, originally developed by Microsoft. It is based on decision tree algorithms and used for ranking, classification and other machine learning tasks...

includes a statistical machine learning library that contains: Boosting Decision tree learning Gradient boosting trees Expectation-maximization algorithm...

(2008). "Decision Tree Ensemble: Small Heterogeneous is Better Than Large Homogeneous" (PDF). 2008 Seventh International Conference on Machine Learning and...

specified by a k-length decision list includes as a subset the language specified by a k-depth decision tree. Learning decision lists can be used for attribute...

Carlo tree search requires a generative model (or an episodic simulator that can be copied at any state), whereas most reinforcement learning algorithms...

on classification charts. Chart Decision tree Decision tree learning Phylogenetic trees Tree of life (biology) Tree structure Wikimedia Commons has media...

In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed...

the package segmented for the R language. A variant of decision tree learning called model trees learns piecewise linear functions. The notion of a piecewise...

In machine learning, grafting is a technique for improving the classification accuracy of a decision tree. A decision tree is a model used to make predictions...

Weka machine learning software described the C4.5 algorithm as "a landmark decision tree program that is probably the machine learning workhorse most...

[A7] In the problem of decision tree learning, Rivest and Laurent Hyafil proved that it is NP-complete to find a decision tree that identifies each of...

is used to construct a Huffman tree during Huffman coding where it finds an optimal solution. In decision tree learning, greedy algorithms are commonly...

objective of machine learning is to minimize uncertainty. Decision tree learning algorithms use relative entropy to determine the decision rules that govern...

assumption of conditional independence. Decision tree learning is a powerful classification technique. The tree tries to infer a split of the training...

statements” and was created with the ID3 algorithm for decision tree learning.: 7 : 348  Rule learning algorithm are taking training data as input and creating...