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What is Decision Tree

Encyclopedia of Information Science and Technology, Second Edition
A symbolic learning classifier that represents a discrete function by a decision tree.
Published in Chapter:
Machine Learning
João Gama (University of Porto, Portugal) and André C.P.L.F. de Carvalho (University of São Paulo, Brazil)
DOI: 10.4018/978-1-60566-026-4.ch392
Abstract
Machine learning techniques have been successfully applied to several real world problems in areas as diverse as image analysis, Semantic Web, bioinformatics, text processing, natural language processing,telecommunications, finance, medical diagnosis, and so forth. A particular application where machine learning plays a key role is data mining, where machine learning techniques have been extensively used for the extraction of association, clustering, prediction, diagnosis, and regression models. This text presents our personal view of the main aspects, major tasks, frequently used algorithms, current research, and future directions of machine learning research. For such, it is organized as follows: Background information concerning machine learning is presented in the second section. The third section discusses different definitions for Machine Learning. Common tasks faced by Machine Learning Systems are described in the fourth section. Popular Machine Learning algorithms and the importance of the loss function are commented on in the fifth section. The sixth and seventh sections present the current trends and future research directions, respectively.
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Applications for Data Mining Techniques in Customer Relationship Management
A visual representation of a decision problem that forms tree-like predictive models with the aim of helping people to make better decisions.
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Identifying Disease and Diagnosis in Females Using Machine Learning
A decision tree is a decision-making aid that employs a tree-like model of choices and their potential results, such as chance event outcomes, cost objects, and usefulness. It’s one approach to show an algorithm made up entirely of conditional control statements.
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Healthcare-Internet of Things and Its Components: Technologies, Benefits, Algorithms, Security, and Challenges
A decision tree is a tree like model of decisions. In decision tree each branch represents the outcome of the test.
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Applying Artificial Intelligence to Financial Investing
A tree like structure for modeling decisions and classifying data.
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Decision Trees
A diagram consisting of square decision nodes, circle probability nodes, and branches representing decision alternatives.
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Utility Functions and Risk Attitudes in Decision Analysis
A graphical representation for a decision problem that specifies the decisions that must be made, the chance events the decision maker faces, and the possible outcomes that result from the decisions and chance events that occurs.
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Evolutionary Algorithms for Global Decision Tree Induction
A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive.
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Artificial Intelligence and Investing
A tree like structure for modeling decisions and classifying data.
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Data Mining Tools: Association Rules
A tool to help make decisions based on a set of rules that help to navigate the tree along its branches.
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Computational Approach for Personality Detection on Attributes: An IoT-MMBD-Enabled Environment
Is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility.
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Particle Swarm Optimization Algorithm as a Tool for Profiling from Predictive Data Mining Models
Decision support method that uses a tree-like graph with algorithm for partitioning.
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Decision Making in Intelligent Agents
A decision tree consists of a root node, representing a decision, a set of intermediate (event) nodes, representing some kind of uncertainty and consequence nodes, representing possible final outcomes. Usually, probability distributions are assigned in the form of weights in the probability nodes as measures of the uncertainties involved.
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RIP Technique for Frequent Itemset Mining
A schematic tree-shaped diagram used to determine a course of action or show a statistical probability. Each branch of the decision tree represents a possible decision or occurrence.
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Application to Bankruptcy Prediction in Banks
A classifier in the form of a tree structure, where each node is either a leaf node or a decision node. A leaf node indicates the value of the target attribute (class) of examples.
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A Study on Supervised Machine Learning Technique to Detect Anomalies in Networks
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Machine Learning for Industrial IoT Systems
Is a flowchart-like structure in which each internal node represents a “test” on an attribute. i.e., whether a coin flip comes up heads or tails, each branch represents the outcome of the test, and each leaf node represents a class label. Decision taken after computing all attributes.
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Applying Artificial Intelligence to Financial Investing
A tree like structure for modeling decisions and classifying data.
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Crime Hotspot Prediction Using Big Data in China
A decision analysis method that, on the basis of the known probability of various situations, calculates the probability of the expected value of the net present value being greater than or equal to zero by constructing the decision tree, evaluates the project risk, and determines its feasibility.
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Introduction to Python and Its Statistical Applications
Decision tree is a decision support tool that uses tree-like model to explore the possible outcomes.
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Data Mining Applications in a Medical System: A Case Study
Decision trees are useful for topics that can be given in the form of unit answers such as category or class name
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A Big Data Analysis of the Factors Influencing Movie Box Office in China
Builds classification models in the form of a tree structure by breaking down a dataset into smaller and smaller subsets, while at the same time, an associated decision tree is incrementally developed. The final result is a tree with decision nodes (having two or more branches) and leaf nodes (representing a classification or decision).
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Decision Tree Applications for Data Modelling
A predictive model which can be visualized in a hierarchical structure using leaves and ramifications.
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Global Induction of Classification and Regression Trees
A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive.
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Autonomous Navigation of Rovers Using ML and DL Techniques
It is amodel where sampling is divided based on the level of the tree, and the mean as well as standard deviation of the target variable are determined at the leaf nodes.
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Airbnb (Air Bed and Breakfast) Listing Analysis Through Machine Learning Techniques
A method that build a tree-like models to present all the possible consequences in different kinds of data is used in the process of data prediction.
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Restaurant Sales Prediction Using Machine Learning
Refers to a tree-shaped model used to make decisions, with each internal node representing a “test” on an attribute, and each leaf node representing a class label.
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Cellular Automata-Basics: Applications in Problem Solving
A graphical representation or plot that reflects the initial states, evolution and possible outcome of a CA that is guided by specified rules.
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Data Mining Tools: Formal Concept Analysis and Rough Sets
A tool to help make decisions based on a set of rules that help to navigate the tree along its branches.
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Financial Sources for Company Scale-Up
this is a scheme in which the person decides according to the given possibilities and then proceeds to the goal to which he/she had established his / her previous selections/decisions.
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Evolutionary Algorithms for Global Decision Tree Induction
A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive.
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Fuzzy Decision Trees
A tree-like way of representing a collection of hierarchical decision rules that lead to a class or value, starting from a root node ending in a series of leaf nodes.
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Data Guided Public Healthcare Decision Making
It is graphical illustration of sequence of actions which result in utility or loss.
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Advances in QoS/E Characterization and Prediction for Next Generation Mobile Communication Systems
Is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm.
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Data Mining in Tourism
It is technique for classifying data. The root node of a decision tree represents all examples. If these examples belong to two or more classes, then the most discriminating attribute is selected and the set is split into multiple classes.
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Guided Interactive Diagnostic Assistance
A rooted, ordered, and labeled tree. Given a boolean function on n-variables (tests), a decision tree is a deterministic algorithm for deciding which variable to test next, based on the previously tested variables and the results of their evaluation, until the function’s value can be determined.
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Introduction to Machine Learning as a New Methodological Framework for Performance Assessment
A machine learning algorithm that cascades data through a series of sequential decisions to determine classification or regression outcomes.
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Fuzzy Decision Trees
A tree-like structure for representing a collection of hierarchical decision rules that lead to a class or value, starting from a root node ending in a series of leaf nodes.
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Explainable Artificial Intelligence for Diagnosis of Cardiovascular Disease
The model divides the sample according to the tree's level, with the leaf nodes serving as the location for determining the target variable's mean and standard deviation.
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Creating and Analyzing Induced Decision Trees From Online Learning Data
A predictive model induced from quantitative and qualitative (nominal, categorical) data.
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Languages and Tools for Rule Modeling
is intuitive way for specifying decision procedures, a simple visual representation (an acyclic, directed graph).
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Fuzzy Decision-Tree-Based Analysis of Databases
It is a tree-like way of representing a collection of hierarchical decision rules that leads to a class or value, starting from a root node ending in a series of leaf nodes.
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Relative Relations in Biomedical Data Classification
A decision tree is a graph that uses a branching method to illustrate every possible outcome of a decision. Each branch of the decision tree represents a possible decision or occurrence. The tree structure shows how one choice leads to the next, and the use of branches indicates that each option is mutually exclusive.
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Machine Learning
A representation of knowledge in a tree-like form usually used for classification. The non-terminal nodes of the tree represent questions, the terminal nodes represent class labels and the edges represent answers to questions.
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Auditor Change Prediction Using Data Mining and Audit Reports
The decision tree summarizes the patterns using the format of the binary tree and it is commonly used in operation management.
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A New Tree-Based Classifier for Satellite Images
It is a decision support tool to display an algorithm that uses a tree-like graph or model of decisions and their possible consequences, including chance event outcomes, resource costs and utility.
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Optimizing Investment Decisions Using DCF, Decision Tree Analysis, and Real Options Analysis: The Case of Hotel Expansions
A decision tree is a tool that uses a tree like graph to model decisions and consequences to help managers incorporate uncertainty in valuations.
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