Decision Trees: 10 Definitions
“Decision trees are a way of representing a series of rules that lead to a class or value. For example, the goal may be to classify a group of householders who have moved to a new house, based on their choice of type of the new dwelling. A simple decision tree can solve this problem and illustrate all the basic components of a decision tree (the decision nodes, branches, and leaves).” (William A V Clark & Marinus C Deurloo, “Categorical Modeling/Automatic Interaction Detection”, Encyclopedia of Social Measurement, 2005)
“A graph of decisions and their possible consequences (including resource costs and risks) used to create a plan to reach a goal. Decision trees are constructed in order to help with making decisions. A decision tree is a special form of tree structure.” (DAMA International, “The DAMA Dictionary of Data Management”, 2011)
“A treelike model of data produced by certain data mining methods. Decision trees can be used for prediction.” (Microsoft, “SQL Server 2012 Glossary”, 2012)
“Decision trees are decision support models that classify patterns using a sequence of well-defined rules. They are tree-like graphs in which each branch node represents an option between a number of alternatives, and each leaf node represents an outcome of the cumulative choices.” (Joo Chuan Tong & Shoba Ranganathan, “Computational T cell vaccine design”, Computer-Aided Vaccine Design, 2013)
“The Decision Tree is a form of flow diagram that helps to map out complicated decision-making processes, or the possible directions a conversation or interaction might take.” (Kevin Duncan, “The Diagrams Book”, 2013)
“Decision tree learning is a supervised machine learning technique for inducing a decision tree from training data. A decision tree (also referred to as a classification tree or a reduction tree) is a predictive model which is a mapping from observations about an item to conclusions about its target value.” (Lin Tan, “The Art and Science of Analyzing Software Data”, 2015)
“An organised pathway of ideas leading to a defined goal, in which at various points, a decision is made about which of two ‘branches’ of ideas to follow to the next decision point.” (K N Krishnaswamy et al, “Management Research Methodology: Integration of Principles, Methods and Techniques”, 2016)
“A decision tree is the arrangement of data in a tree structure where, at each node, data is separated into different branches according to the value of the attribute at the node.” (David Natingga, “Data Science Algorithms in a Week” 2nd Ed., 2018)
“Decision trees are a machine learning algorithm that predicts the value of a target variable based on decision rules learned from training data. The algorithm can be applied to both regression and classification problems by changing the objective function that governs how the tree learns the decision rules.” (Stefan Jansen, “Hands-On Machine Learning for Algorithmic Trading”, 2018)
“In a machine learning context, a decision tree is a data structure that is built for classification or regression tasks. Each node in the tree splits on a particular feature.” (Alex Thomas, “Natural Language Processing with Spark NLP”, 2020)
More definitions on “Decision Trees” at sql-troubles.blogspot.com.
