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...

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

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...

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

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

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...

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...

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

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...

hybridization of hypotheses generated from diverse base learning algorithms, such as combining decision trees with neural networks or support vector machines...

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

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

of competitive learning include vector quantization and self-organizing maps (Kohonen maps). Machine learning Decision tree learning Pattern recognition...

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...

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...

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

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

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...

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

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

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

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

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...