Decision Tree Training System

A Decision Tree Training System is a supervised model-based training system that implements a decision tree training algorithm to solve a decision tree training task.

• AKA: Decision Tree Inducer/Learner.
• Context:
  • It can range from being a Classification Tree Training System, to being a Ranking Tree Training System, to being a Regression Tree Training System.
  • It can range from being a On-Premise Decision Tree Learning System to being a Decision Tree Predictive Modeling Service,
  • It can range from being a Single-Model Decision Tree Training System to being an Ensemble-Model Decision Tree Training System.
  • It can range from being a Fully-Supervised Decision Tree Training System to being a Semi-Supervised Decision Tree Training System.
  • …
• Example(s):
  • a Classification Tree Learning System, such as: C4.5, rpart, sklearn.tree.DecisionTreeClassifier, or spark.ml.classification.DecisionTreeClassifier.
  • a Ranking Tree Learning System, such as: ...
  • a Regression Tree Learning System, such as: rpart or sklearn.tree.DecisionTreeRegressor
  • a Decision Tree Predictive Modeling Service, such as: BigML's predictive modeling service.
  • a Model Trees Learning System.
  • …
• Counter-Example(s):
  • an SVM Training System.
  • a GLM Training System.
  • a Naive-Bayes Training System.
• See: Decision Tree Inference System.

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References

2017a

• (Scikit-Learn, 2017) & "1.10. Decision Trees" http://scikit-learn.org/stable/modules/tree.html Retrieved on 2017-10-15
  • QUOTE: Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features.

    For instance, in the example below, decision trees learn from data to approximate a sine curve with a set of if-then-else decision rules. The deeper the tree, the more complex the decision rules and the fitter the model.

    Figure:Decision Tree Regression

2017b

• (Wikipedia, 2017) ⇒ https://en.wikipedia.org/wiki/Decision_tree_learning Retrieved:2017-10-15.
  • Decision tree learning uses a decision tree (as a predictive model) to go from observations about an item (represented in the branches) to conclusions about the item's target value (represented in the leaves). It is one of the predictive modelling approaches used in statistics, data mining and machine learning. Tree models where the target variable can take a discrete set of values are called classification trees ; in these tree structures, leaves represent class labels and branches represent conjunctions of features that lead to those class labels. Decision trees where the target variable can take continuous values (typically real numbers) are called regression trees.

    In decision analysis, a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree describes data (but the resulting classification tree can be an input for decision making). This page deals with decision trees in data mining.

2017c

• (Furnkranz, 2017) ⇒ Johannes Fürnkranz, (2017). "Decision Tree". In Encyclopedia of Machine Learning and Data Mining pp 330-335.
  • ABSTRACT: The induction of decision trees is one of the oldest and most popular techniques for learning discriminatory models, which has been developed independently in the statistical (Breiman et al. 1984; Kass 1980 ) and machine learning (Hunt et al. 1966 ; Quinlan 1983, 1986) communities. A decision tree is a tree-structured classification model, which is easy to understand, even by non-expert users, and can be efficiently induced from data. An extensive survey of decision-tree learning can be found in Murthy (1998).
  • QUOTE: Decision trees are also often used as components in Ensemble Methods such as random forests (Breiman 2001 ) or AdaBoost (Freund and Schapire 1996). They can also be modified for predicting numerical target variables, in which case they are known as regression trees. One can also put more complex prediction models into the leaves of a tree, resulting in Model Trees.

2011

• (Loh, 2011) ⇒ Loh, W. Y. (2011). Classification and regression trees. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 1(1), 14-23.
  • ABSTRACT: Classification and regression trees are machine-learning methods for constructing prediction models from data. The models are obtained by recursively partitioning the data space and fitting a simple prediction model within each partition. As a result, the partitioning can be represented graphically as a decision tree. Classification trees are designed for dependent variables that take a finite number of unordered values, with prediction error measured in terms of misclassification cost. Regression trees are for dependent variables that take continuous or ordered discrete values, with prediction error typically measured by the squared difference between the observed and predicted values. This article gives an introduction to the subject by reviewing some widely available algorithms and comparing their capabilities, strengths, and weakness in two examples.

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