Decision Tree Training Task

A Decision Tree Training Task is a decision tree creation task that is a model-based supervised learning task (required to produce a predictor tree).

• AKA: Predictor Tree Learning.
• Context:
  • It can be solved by a Decision Tree Learning System (that implements a dtree training algorithm).
  • It can range from being a Classification Tree Learning Task, to being a Ranking Tree Learning Task, to being a Regression Tree Learning Task.
  • …
• Example(s):
  • a Random Forests Learning Task.
  • a Classification Tree Training Task.
  • a Regression Tree Training Task.
  • a Decision Tree Ensemble Learning Task.
  • …
• Counter-Example(s):
  • a Heuristic Decision Tree Creation Task.
  • a k-NN Learning Task.
  • an SVM Learning Task.
• See: Pruning Heuristic, Classification Task, Regression Task, Supervised Learning Task, Unsupervised Learning Task.

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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.

2013

• (Berrar & Dubitzky, 2013) ⇒ Daniel Berrar, and Werner Dubitzky. (2013), "Decision Tree". In: Encyclopedia of Systems Biology pp 551-555
  • QUOTE: A decision tree refers to both a concrete decision model used to support decision making and a method to construct such models automatically from data. As a model, a decision tree refers to a concrete information or knowledge structure to support decision making, such as classification (Model Testing, Machine Learning) and regression (Regression Analysis) tasks, processes or analyses. As a method, a decision tree comprises various techniques to construct decision tree models in a highly automated fashion using specific algorithms and measures from the field of statistics, machine learning (Model Validation, Machine Learning) and artificial intelligence.

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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