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...

at each stage) is called a decision tree, and when applied in the area of machine learning is known as decision tree learning. Usually an attribute with...

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...

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

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

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

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

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

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...

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

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

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

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

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

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

A decision stump is a machine learning model consisting of a one-level decision tree. That is, it is a decision tree with one internal node (the root)...

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...

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

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

AdaBoost (with decision trees as the weak learners) is often referred to as the best out-of-the-box classifier. When used with decision tree learning, information...

environment is typically stated in the form of a Markov decision process (MDP), as many reinforcement learning algorithms use dynamic programming techniques. The...

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

Chi-square automatic interaction detection (CHAID) is a decision tree technique based on adjusted significance testing (Bonferroni correction, Holm-Bonferroni...

album), 1994 Impurity (New Model Army album), 1990 Gini impurity, in decision tree learning Purity Ritual impurity Aśuddhatā, in Hindu religion Dirty Unclean...

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...

computational agent learning to make decisions by trial and error. Deep RL incorporates deep learning into the solution, allowing agents to make decisions from unstructured...

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

In decision tree learning, information gain ratio is a ratio of information gain to the intrinsic information. It was proposed by Ross Quinlan, to reduce...

regression (LR) and decision tree learning. Logistic model trees are based on the earlier idea of a model tree: a decision tree that has linear regression models...