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Topic: Decision tree

Related Topics

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Decision making

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Machine learning

Supervised learning

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Data mining

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Model (economics)

Support vector machine

Pareto analysis

	Decision tree - Wikipedia, the free encyclopedia
		In decision theory, a decision tree is a graph of decisions and their possible consequences, (including resource costs and risks) used to create a plan to reach a goal.
		Decision tree can be described also as the synergy of mathematical and computing techniques that aids on the description, categorisation and generalisation of a given set of data.
		A decision tree is a model of the data that encodes the distribution of the class label (again the Y) in terms of the predictor attributes.
	en.wikipedia.org /wiki/Decision_tree (1417 words)

	Decision Tree Forests
		Decision tree forest models often have a degree of accuracy that cannot be obtained using a large, single-tree model.
		Decision tree forests have two stochastic (randomizing) elements: (1) the selection of data rows used as input for each tree, and (2) the set of predictor variables considered as candidates for each node split.
		Decision tree forest models often can provide greater predictive accuracy than single-tree models, but they have the disadvantage that you cannot visualize them the way you can a single tree; decision tree forest models are more of a “fl box”.
	www.dtreg.com /treeforest.htm (1821 words)

	DMS Tutorial - Decision trees (Site not responding. Last check: 2007-10-22)
		A decision tree can be used to classify an example by starting at the root of the tree and moving through it until a leaf node, which provides the classification of the instance.
		Decision trees are less appropriate for estimation tasks where the goal is to predict the value of a continuous attribute.
		Decision trees are prone to errors in classification problems with many class and relatively small number of training examples.
	dms.irb.hr /tutorial/tut_dtrees.php (2828 words)

	Decision Tree
		A decision tree is a graphical diagram consisting of nodes and branches.
		Using a decision tree like the one shown at the end of this article, Gerber concluded that its best option was to be proactive and initiate its own solutions without waiting for the CPSC report.
		As this example shows, decision trees enable managers to use available data to conceptualize and articulate possible scenarios of future events even though important pieces of information may still be missing.
	gbr.pepperdine.edu /993/tree.html (1514 words)

	Decision Tree Learning Help
		Decision trees are a simple yet successful technique for supervised classification learning.
		Before building a decision tree, the data set can be viewed, and examples can be moved to and from the training set and test set.
		Before automatically creating a decision tree, you can choose from several splitting functions that are used to determine which attribute to split on.
	www.cs.ubc.ca /nest/lci/CIspace/Version4/dTree/help/general.html (1913 words)

	Estard Software :: Decision Trees (Site not responding. Last check: 2007-10-22)
		In decision theory, a decision tree is a graph of decisions and their possible consequences, used to create a plan to reach a goal.
		A decision tree consists of nodes where a logical decision has to be made and connecting branches that are chosen according to the result of this decision.
		Once a decision tree solution is generated from the learning data, it can be used for predicting or estimating the class of a new case.
	www.estard.com /decisiontree/decision_trees_definition.asp (563 words)

	Decision Trees
		Also see their Decision Trees Applet: "Learning is the ability to improve one's behaviour based on experience and represents an important element of computational intelligence.
		Although rules and decision trees may seem similar at first, they are in fact quite different both in terms of the information they discover from databases and in terms of their behavior on new data items.
		Decision trees are attractive because they show clearly how to reach a decision, and because they are easy to construct automatically from labeled instances.
	www.aaai.org /AITopics/html/trees.html (1036 words)

	ONLamp.com -- Building Decision Trees in Python
		Decision trees are mainly used for classification purposes, but they are also helpful in uncovering features of data that were previously unrecognizable to the eye.
		A decision tree is essentially a series of if-then statements, that, when applied to a record in a data set, results in the classification of that record.
		Given the decision tree in Figure 1 and the set of data, it should be somewhat easy to see just how a decision tree can classify records in a data set.
	www.onlamp.com /pub/a/python/2006/02/09/ai_decision_trees.html?page=1 (1240 words)

	DTREG -- Predictive Modeling Software
		A decision tree is an excellent tool for this type of analysis because it shows which combination of attributes best predict the purchase of the product.
		And, a decision tree or SVM model can be used to “score” a set of individuals and rank them by the probability that they will respond positively to a marketing effort.
		Once a decision tree has been built, you can use DTREG to "score" a new dataset and predict values for the target variable.
	www.dtreg.com (1611 words)

	Machine Learning/Inductive Inference/Decision Trees/Overview
		Decision tree learning methods are robust to errors - both errors in classifications of the training examples and errors in the attribute values that describe these examples.
		Decision tree methods can be used even when some training examples have unknown values (e.g., humidity is known for only a fraction of the examples).
		An instance is classified by starting at the root node of the decision tree, testing the attribute specified by this node, then moving down the tree branch corresponding to the value of the attribute.
	www.cs.uregina.ca /~dbd/cs831/notes/ml/dtrees/4_dtrees1.html (557 words)

	Data Mining Algorithm- Decision Tree Algorithm
		The target attribute of a Decision Tree exploration must be of a Boolean (yes/no) or categorical data type.
		The Decision Tree exploration engine is used for task such as classifying records or predicting outcomes.
		You should use decision trees when you goal is to assign your records to a few broad categories.
	www.megaputer.com /products/pa/algorithms/dt.php3 (417 words)

	Digital Preservation Coalition - Handbook - Decision Tree - Selection of Content and Version
		Note: The Decision Tree has been modified and updated by Deborah Woodyard-Robinson and the interactive version and PDF prepared by The Silk-Route.
		This Decision Tree may be used as a tool to construct or test such a policy for your organisation.
		The decision process represented in the tree should be addressed by your policy for selection of digital materials for the long-term.
	www.dpconline.org /graphics/handbook/dec-tree.html (287 words)

	Machine Learning/Inductive Inference/Decision Trees/Construction (Site not responding. Last check: 2007-10-22)
		Trees that place high information gain attributes close to the root are preferred over those that do not.
		Because every finite discrete-valued function can be represented by some decision tree, ID3 avoids one of the major risks of methods that search incomplete hypothesis spaces (such as version space methods that consider only conjunctive hypotheses): that the hypothesis space might not contain the target function.
		For example, it does not have the ability to determine how many alternative decision trees are consistent with the available training data, or to pose new instance queries that optimally resolve among these competing hypotheses.
	www.cs.uregina.ca /~hamilton/courses/831/notes/ml/dtrees/4_dtrees2.html (893 words)

	COSI113B Decision Tree Learning (Site not responding. Last check: 2007-10-22)
		A decision tree is a tree which attempts to provide an answers for a specified global question, like "should we play tennis today?".
		A learning algorithm for a Decision Tree is an algorithm that can takes a global question as well as example data that relates to that question and translate that data into a Decision Tree which answers the question.
		Generally in a decision tree learning problem one assumes that the questions (or attributes) and their possible answers are given, as determining the attributes a in the general case is clearly AI complete.
	www.cs.brandeis.edu /~cs113/classprojects/~kristian/cs113/DecisionTrees.html (2003 words)

	Mind Tools - Decision Theory and Decision Trees
		Decision trees are excellent tools for making financial or number based decisions where a lot of complex information needs to be taken into account.
		This decision is represented by a small square towards the left of a large piece of paper.
		Note that the tree looks less confused when different colours are used for numbers than for the structure of the tree.
	www.psychwww.com /mtsite/dectree.html (1048 words)

	Drill-down Decision Tree Software - Decision Tree Classification
		Decision tree classifier is a predictive model that, as its name suggests, is presented in a tree.
		Decision trees are constructed by splitting (or dividing) tree nodes repeatedly.
		Decision tree classification is extremely sensitive to data skews.
	www.roselladb.com /decision-tree-classification.htm (1075 words)

	Classification Tree in Excel
		If you are looking at Decision trees for the first time, you need to familiarize yourself a bit on the jargons.
		While growing the tree, at any point a predictor is chosen to split a node such that the Information Gain is maximized after the split.
		The decision on whether or not to drop a clause is taken based on the outcome of a statistical independence test.
	www.geocities.com /adotsaha/CTree/CtreeinExcel.html (1942 words)

	Problems and Examples
		A Decision tree takes as input an object or situation described by a set of attributes and returns a “decision” which is the the predicted output value for the input.
		A decision tree reaches its decision by performing a sequence of tests.
		Your output should be a backward traversal/interpretation of the decision tree that you internally build during the implementation of the Decision Tree Algorithm.
	homepages.udayton.edu /~seitzeje/cps481/lab3_decision_tree.htm (564 words)

	Occam's razor - Wikipedia, the free encyclopedia
		When it is proposed as a maxim of science, Occam's razor is construed as a decision procedure for choosing among competing systems of hypotheses.
		For some types of tree, it will consistently produce the wrong results regardless of how much data is collected (this is called long branch attraction).
		Tests of Occam's razor on decision tree models which initially appeared criticial have been shown to actually work fine when re-visited using MML.
	en.wikipedia.org /wiki/Occam%27s_Razor (4942 words)

	Decision Tree Bean
		Each path from the root of the tree to a leaf node is a rule made by the conjunction of all feature values found along the path.
		A decision tree is built by recursively partitioning the training set until examples on each partition belong to the same class.
		In the training phase, the algorithm constructs the decision tree based on the training data.
	www.research.ibm.com /able/doc/reference/com/ibm/able/beans/knn/doc-files/DecisionTree.html (560 words)

	Decision tree - Wikipedia, the free encyclopedia
		In decision theory (for example risk management), a decision tree is a graph of decisions and their possible consequences, (including resource costs and risks) used to create a plan to reach a goal.
		In machine learning, a decision tree is a predictive model; that is, a mapping of observations about an item to conclusions about the item's target value.
		The conclusion is that decision tree helped us turn a complex data representation into a much easier structure (parsimonious).
	www.soton.ac.uk /~evandro/proj/spartacus (982 words)

	Decision Tree Software > Decision Tree Analysis, DecisionPro Decision Tree Software (Site not responding. Last check: 2007-10-22)
		Decision tree software is used to select the best course of action in cases where you face uncertainty as in, for example, deciding how much inventory to build when sales forecasts are uncertain; or, choosing between litigation and an out-of-court settlement.
		Once you have modeled a decision using DecisionPro decision tree software, it will automatically analyze the tree to help you better understand and communicate the risks involved.
		For example, DecisionPro decision tree analysis constructs a risk profile graph or table illustrating all possible outcomes and probabilities.
	www.vanguardsw.com /decisionpro/decision-tree-software.htm (209 words)

	Using the Decision Tree Applet
		This applet includes 3 panels: the upper left is used to view the current dataset; the upper right refers to the algorithm being executed; and the bottom shows the decision tree that is grown.
		You can also zoom tree to various levels; note the actual attribute/class value is only present on the largest (100%) zoom level.
		When clicking on a node in the decision tree, those records are shaded "Yellow".
	www.cs.ualberta.ca /~aixplore/learning/DecisionTrees/InterArticle/DecisionTreeDoc.html (881 words)

	Supertree Decision Analysis Software
		Decision analysis is a comprehensive process for making sound decisions in the face of uncertainty.
		Developed by the founders of decision analysis, Supertree is one of the most widely used professional decision analysis computer programs.
		The program evaluates decision trees, determines the risk associated with any decision alternative in the tree, and identifies the best alternative for decision makers.
	www.supertree.net (483 words)

	Decision Tree
		A decision tree is a map of the reasoning process.
		The following decision tree assumes that questions are answered with a certain yes or no. A tree that allows answering with a partial yes or no would have a much larger number of end nodes.
		Select the decision tree by positioning the cursor over the tree and then click the right mouse button to copy it.
	www.eskimo.com /~mighetto/lstree.htm (191 words)

	Decision Tree Analysis - Decision Trees from Mind Tools
		Decision Trees are excellent tools for helping you to choose between several courses of action.
		Where you are calculating the value of uncertain outcomes (circles on the diagram), do this by multiplying the value of the outcomes by their probability.
		As with all Decision Making methods, decision tree analysis should be used in conjunction with common sense - decision trees are just one important part of your Decision Making tool kit.
	www.mindtools.com /dectree.html (2012 words)

	National Patient Safety Agency \| Incident Decision Tree,National Patient Safety Agency,Health professional zone
		The Incident Decision Tree (click to launch Incident Decision Tree) is a key component of the National Patient Safety Agency’s (NPSA) drive to help the NHS move away from asking “Who was to blame?” to “Why did the individual act in this way?” when things go wrong.
		The Incident Decision Tree has been created to help NHS managers and senior clinicians decide whether they need to suspend (exclude) staff involved in a serious patient safety incident and to identify appropriate management action. The aim is to promote fair and consistent staff treatment within and between healthcare organisations.
		The Incident Decision Tree complements the NPSA’s Root Cause Analysis toolkit and the two can be used in parallel.
	www.npsa.nhs.uk /health/resources/incident_decision_tree (237 words)

	Decision Trees
		The optimal decision is to select B. A convenient way to represent this problem is through the use of decision trees, as in Figure 9.1.
		Using a decision tree to find the optimal decision is called solving the tree.
		As we continue to move to the right of the decision tree, the next nodes are square, corresponding to the three marketing and production strategies.
	mat.gsia.cmu.edu /QUANT/notes/node75.html (801 words)

	TreePlan, SensIt, and RiskSim Add-Ins for Excel
		You will be able to use Excel to analyze a variety of decision problems, and you save 20% when you purchase this bundle instead of the individual add-ins.
		Decision trees are useful for analyzing sequential decision problems under uncertainty, i.e., linked decisions.
		In a decision situation, sensitivity analysis helps you determine which of your input assumptions are critical so that you know where to focus your effort for gathering more information or reducing uncertainty.
	www.treeplan.com (343 words)

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