What is Decision Trees

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Decision tree is a classifier in the form of a tree structure, where each node is either a leaf node or a decision node. A decision tree can be used to classify an instance by starting at the root of the tree and moving through it until a leaf node, which provides the classification of the instance. A well known and frequently used algorithm of decision tree over the years is C4.5 Learn more in: Improving the Naïve Bayes Classifier

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It is a supervised learning model. It learns the decision rules from the training data to classify an unknown test example. Learn more in: Deep-Auto Encoders for Detecting Credit Card Fraud

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A predictive model comprised of multiple steps at which each step, the target variable with a value of 1 is assigned to one branch while the target variable with a value of 0 is assigned to the other branch based on the value of another variable in the data set. Learn more in: Visualization of Predictive Modeling for Big Data Using Various Approaches When There Are Rare Events at Differing Levels

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Classifier consisting on an arboreal structure. A test sample is classified by evaluating it in each node, starting at the top one and choosing a specific branch depending on this evaluation. The classification of the sample is the class assigned in the bottom node. Learn more in: Emulating Subjective Criteria in Corpus Validation

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Tree-shaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset. Learn more in: Neural Networks for Automobile Insurance Pricing

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A method of finding rules or (rule induction) which divide the data into subgroups which are as similar as possible with regard to a target variable (variable that we want to explain). Learn more in: Principles on Symbolic Data Analysis

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A decision tree is made up of nodes corresponding to the attributes of the selected objects and of branches characterizing the alternative values of these attributes. The leaves of the tree represent the sets of objects of the same class. The construction of decision trees is a top-down generalization approach. The QUINLAN method (ID3 algorithms) consists of successively testing each attribute to know which one to use first so as to optimize the information gain, that is, the attribute which best allows to distinguish the examples of different classes. Learn more in: Decision Support Approach for Assessing of Rail Transport: Methods Based on AI and Machine Learning

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As a hierarchical collection of rules that describe how to divide a large collection of records into successively smaller group of records. With each successive division, the member of the resulting segment became more and more similar to one another with respect to the dependent variable. Learn more in: Customer Relationship Management and Data Mining: A Classification Decision Tree to Predict Customer Purchasing Behavior in Global Market

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Predictive models that map the observations about an event to infer about its target value. Learn more in: Computational Models for the Analysis of Modern Biological Data