Decision tree learning
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Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. 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 finite 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 not decisions; rather the resulting classification tree can be an input for decision making. This page deals with decision trees in data mining.
Contents
General
Decision tree learning is a method commonly used in data mining.^{[1]} The goal is to create a model that predicts the value of a target variable based on several input variables. An example is shown on the right. Each interior node corresponds to one of the input variables; there are edges to children for each of the possible values of that input variable. Each leaf represents a value of the target variable given the values of the input variables represented by the path from the root to the leaf.
A decision tree is a simple representation for classifying examples. For this section, assume that all of the features have finite discrete domains, and there is a single target feature called the classification. Each element of the domain of the classification is called a class. A decision tree or a classification tree is a tree in which each internal (nonleaf) node is labeled with an input feature. The arcs coming from a node labeled with a feature are labeled with each of the possible values of the feature. Each leaf of the tree is labeled with a class or a probability distribution over the classes.
A tree can be "learned" by splitting the source set into subsets based on an attribute value test. This process is repeated on each derived subset in a recursive manner called recursive partitioning. The recursion is completed when the subset at a node has all the same value of the target variable, or when splitting no longer adds value to the predictions. This process of topdown induction of decision trees (TDIDT) ^{[2]} is an example of a greedy algorithm, and it is by far the most common strategy for learning decision trees from data.
In data mining, decision trees can be described also as the combination of mathematical and computational techniques to aid the description, categorisation and generalisation of a given set of data.
Data comes in records of the form:
The dependent variable, Y, is the target variable that we are trying to understand, classify or generalize. The vector x is composed of the input variables, x_{1}, x_{2}, x_{3} etc., that are used for that task.
Types
Decision trees used in data mining are of two main types:
 Classification tree analysis is when the predicted outcome is the class to which the data belongs.
 Regression tree analysis is when the predicted outcome can be considered a real number (e.g. the price of a house, or a patient’s length of stay in a hospital).
The term Classification And Regression Tree (CART) analysis is an umbrella term used to refer to both of the above procedures, first introduced by Breiman et al.^{[3]} Trees used for regression and trees used for classification have some similarities  but also some differences, such as the procedure used to determine where to split.^{[3]}
Some techniques, often called ensemble methods, construct more than one decision tree:
 Bagging decision trees, an early ensemble method, builds multiple decision trees by repeatedly resampling training data with replacement, and voting the trees for a consensus prediction.^{[4]}
 A Random Forest classifier uses a number of decision trees, in order to improve the classification rate.
 Boosted Trees can be used for regressiontype and classificationtype problems.^{[5]}^{[6]}
 Rotation forest  in which every decision tree is trained by first applying principal component analysis (PCA) on a random subset of the input features.^{[7]}
Decision tree learning is the construction of a decision tree from classlabeled training tuples. A decision tree is a flowchartlike structure, where each internal (nonleaf) node denotes a test on an attribute, each branch represents the outcome of a test, and each leaf (or terminal) node holds a class label. The topmost node in a tree is the root node.
There are many specific decisiontree algorithms. Notable ones include:
 ID3 (Iterative Dichotomiser 3)
 C4.5 (successor of ID3)
 CART (Classification And Regression Tree)
 CHAID (CHisquared Automatic Interaction Detector). Performs multilevel splits when computing classification trees.^{[8]}
 MARS: extends decision trees to handle numerical data better.
 Conditional Inference Trees. Statisticsbased approach that uses nonparametric tests as splitting criteria, corrected for multiple testing to avoid overfitting. This approach results in unbiased predictor selection and does not require pruning.^{[9]}^{[10]}
ID3 and CART were invented independently at around the same time (between 1970 and 1980)^{[citation needed]}, yet follow a similar approach for learning decision tree from training tuples.
Metrics
Algorithms for constructing decision trees usually work topdown, by choosing a variable at each step that best splits the set of items.^{[11]} Different algorithms use different metrics for measuring "best". These generally measure the homogeneity of the target variable within the subsets. Some examples are given below. These metrics are applied to each candidate subset, and the resulting values are combined (e.g., averaged) to provide a measure of the quality of the split.
Gini impurity
Used by the CART (classification and regression tree) algorithm, Gini impurity is a measure of how often a randomly chosen element from the set would be incorrectly labeled if it were randomly labeled according to the distribution of labels in the subset. Gini impurity can be computed by summing the probability of each item being chosen times the probability of a mistake in categorizing that item. It reaches its minimum (zero) when all cases in the node fall into a single target category.
To compute Gini impurity for a set of items, suppose , and let be the fraction of items labeled with value in the set.
Information gain
Used by the ID3, C4.5 and C5.0 treegeneration algorithms. Information gain is based on the concept of entropy from information theory.
Variance reduction
Introduced in CART,^{[3]} variance reduction is often employed in cases where the target variable is continuous (regression tree), meaning that use of many other metrics would first require discretization before being applied. The variance reduction of a node N is defined as the total reduction of the variance of the target variable x due to the split at this node:
where , , and are the set of presplit sample indices, set of sample indices for which the split test is true, and set of sample indices for which the split test is false, respectively. Each of the above summands are indeed variance estimates, though, written in a form without directly referring to the mean.
Decision tree advantages
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Amongst other data mining methods, decision trees have various advantages:
 Simple to understand and interpret. People are able to understand decision tree models after a brief explanation.
 Requires little data preparation. Other techniques often require data normalisation, dummy variables need to be created and blank values to be removed.
 Able to handle both numerical and categorical data. Other techniques are usually specialised in analysing datasets that have only one type of variable. (For example, relation rules can be used only with nominal variables while neural networks can be used only with numerical variables.)
 Uses a white box model. If a given situation is observable in a model the explanation for the condition is easily explained by boolean logic. By contrast, in a black box model, the explanation for the results is typically difficult to understand, for example with an artificial neural network.
 Possible to validate a model using statistical tests. That makes it possible to account for the reliability of the model.
 Robust. Performs well even if its assumptions are somewhat violated by the true model from which the data were generated.
 Performs well with large datasets. Large amounts of data can be analysed using standard computing resources in reasonable time.
Limitations
 The problem of learning an optimal decision tree is known to be NPcomplete under several aspects of optimality and even for simple concepts.^{[12]}^{[13]} Consequently, practical decisiontree learning algorithms are based on heuristics such as the greedy algorithm where locallyoptimal decisions are made at each node. Such algorithms cannot guarantee to return the globallyoptimal decision tree. To reduce the greedy effect of localoptimality some methods such as the dual information distance (DID) tree were proposed.^{[14]} [1]
 Decisiontree learners can create overcomplex trees that do not generalise well from the training data. (This is known as overfitting.^{[15]}) Mechanisms such as pruning are necessary to avoid this problem (with the exception of some algorithms such as the Conditional Inference approach, that does not require pruning ^{[9]}^{[10]}).
 There are concepts that are hard to learn because decision trees do not express them easily, such as XOR, parity or multiplexer problems. In such cases, the decision tree becomes prohibitively large. Approaches to solve the problem involve either changing the representation of the problem domain (known as propositionalisation)^{[16]} or using learning algorithms based on more expressive representations (such as statistical relational learning or inductive logic programming).
 For data including categorical variables with different numbers of levels, information gain in decision trees is biased in favor of those attributes with more levels.^{[17]} However, the issue of biased predictor selection is avoided by the Conditional Inference approach.^{[9]}
Extensions
Decision graphs
In a decision tree, all paths from the root node to the leaf node proceed by way of conjunction, or AND. In a decision graph, it is possible to use disjunctions (ORs) to join two more paths together using Minimum message length (MML).^{[18]} Decision graphs have been further extended to allow for previously unstated new attributes to be learnt dynamically and used at different places within the graph.^{[19]} The more general coding scheme results in better predictive accuracy and logloss probabilistic scoring.^{[citation needed]} In general, decision graphs infer models with fewer leaves than decision trees.
Alternative search methods
Evolutionary algorithms have been used to avoid local optimal decisions and search the decision tree space with little a priori bias.^{[20]}^{[21]}
It is also possible for a tree to be sampled using MCMC.^{[22]}
The tree can be searched for in a bottomup fashion.^{[23]}
See also
 Decision tree pruning
 Binary decision diagram
 CHAID
 CART
 ID3 algorithm
 C4.5 algorithm
 Decision stump
 Incremental decision tree
 Alternating decision tree
 Structured data analysis (statistics)
 Logistic model tree
Implementations
Many data mining software packages provide implementations of one or more decision tree algorithms. Several examples include Salford Systems CART (which licensed the proprietary code of the original CART authors^{[3]}), IBM SPSS Modeler, RapidMiner, SAS Enterprise Miner, Matlab, R (an open source software environment for statistical computing which includes several CART implementations such as rpart, party and randomForest packages), Weka (a free and opensource data mining suite, contains many decision tree algorithms), Orange (a free data mining software suite, which includes the tree module orngTree), KNIME, Microsoft SQL Server [2], and scikitlearn (a free and opensource machine learning library for the Python programming language).
References
 ↑ Rokach, Lior; Maimon, O. (2008). Data mining with decision trees: theory and applications. World Scientific Pub Co Inc. ISBN 9789812771711.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Quinlan, J. R., (1986). Induction of Decision Trees. Machine Learning 1: 81106, Kluwer Academic Publishers
 ↑ ^{3.0} ^{3.1} ^{3.2} ^{3.3} Breiman, Leo; Friedman, J. H.; Olshen, R. A.; Stone, C. J. (1984). Classification and regression trees. Monterey, CA: Wadsworth & Brooks/Cole Advanced Books & Software. ISBN 9780412048418.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Breiman, L. (1996). Bagging Predictors. "Machine Learning, 24": pp. 123140.
 ↑ Friedman, J. H. (1999). Stochastic gradient boosting. Stanford University.
 ↑ Hastie, T., Tibshirani, R., Friedman, J. H. (2001). The elements of statistical learning : Data mining, inference, and prediction. New York: Springer Verlag.
 ↑ Rodriguez, J.J. and Kuncheva, L.I. and Alonso, C.J. (2006), Rotation forest: A new classifier ensemble method, IEEE Transactions on Pattern Analysis and Machine Intelligence, 28(10):16191630.
 ↑ Kass, G. V. (1980). "An exploratory technique for investigating large quantities of categorical data". Applied Statistics. 29 (2): 119–127. doi:10.2307/2986296. JSTOR 2986296.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ ^{9.0} ^{9.1} ^{9.2} Hothorn, T.; Hornik, K.; Zeileis, A. (2006). "Unbiased Recursive Partitioning: A Conditional Inference Framework". Journal of Computational and Graphical Statistics. 15 (3): 651–674. doi:10.1198/106186006X133933. JSTOR 27594202.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ ^{10.0} ^{10.1} Strobl, C.; Malley, J.; Tutz, G. (2009). "An Introduction to Recursive Partitioning: Rationale, Application and Characteristics of Classification and Regression Trees, Bagging and Random Forests". Psychological Methods. 14 (4): 323–348. doi:10.1037/a0016973.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Rokach, L.; Maimon, O. (2005). "Topdown induction of decision trees classifiersa survey". IEEE Transactions on Systems, Man, and Cybernetics, Part C. 35 (4): 476–487. doi:10.1109/TSMCC.2004.843247.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Hyafil, Laurent; Rivest, RL (1976). "Constructing Optimal Binary Decision Trees is NPcomplete". Information Processing Letters. 5 (1): 15–17. doi:10.1016/00200190(76)900958.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ Murthy S. (1998). Automatic construction of decision trees from data: A multidisciplinary survey. Data Mining and Knowledge Discovery
 ↑ BenGal I. Dana A., Shkolnik N. and Singer (20). "Efficient Construction of Decision Trees by the Dual Information Distance Method" (PDF). Quality Technology & Quantitative Management (QTQM), 11( 1), 133147. Cite journal requires
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(help)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>  ↑ "Principles of Data Mining". 2007. doi:10.1007/9781846287664. ISBN 9781846287657. Cite journal requires
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(help)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>  ↑ Horváth, Tamás; Yamamoto, Akihiro, eds. (2003). "Inductive Logic Programming". Lecture Notes in Computer Science. 2835. doi:10.1007/b13700. ISBN 9783540201441. Cite journal requires
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(help)<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>  ↑ Deng,H.; Runger, G.; Tuv, E. (2011). Bias of importance measures for multivalued attributes and solutions. Proceedings of the 21st International Conference on Artificial Neural Networks (ICANN). pp. 293–300.<templatestyles src="Module:Citation/CS1/styles.css"></templatestyles>
 ↑ http://citeseer.ist.psu.edu/oliver93decision.html
 ↑ Tan & Dowe (2003)
 ↑ Papagelis A., Kalles D.(2001). Breeding Decision Trees Using Evolutionary Techniques, Proceedings of the Eighteenth International Conference on Machine Learning, p.393400, June 28July 01, 2001
 ↑ Barros, Rodrigo C., Basgalupp, M. P., Carvalho, A. C. P. L. F., Freitas, Alex A. (2011). A Survey of Evolutionary Algorithms for DecisionTree Induction. IEEE Transactions on Systems, Man and Cybernetics, Part C: Applications and Reviews, vol. 42, n. 3, p. 291312, May 2012.
 ↑ Chipman, Hugh A., Edward I. George, and Robert E. McCulloch. "Bayesian CART model search." Journal of the American Statistical Association 93.443 (1998): 935948.
 ↑ Barros R. C., Cerri R., Jaskowiak P. A., Carvalho, A. C. P. L. F., A bottomup oblique decision tree induction algorithm. Proceedings of the 11th International Conference on Intelligent Systems Design and Applications (ISDA 2011).
External links
 Building Decision Trees in Python From O'Reilly.
 An Addendum to "Building Decision Trees in Python" From O'Reilly.
 Decision Trees Tutorial using Microsoft Excel.
 Decision Trees page at aitopics.org, a page with commented links.
 Decision tree implementation in Ruby (AI4R)
 Evolutionary Learning of Decision Trees in C++
 Java implementation of Decision Trees based on Information Gain
 A very explicit explanation of information gain as splitting criterion