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*  Decision analysis
Graphical representation of decision analysis problems commonly use framing tools, influence diagrams and decision trees. Such ... Choice Decision analysis cycle Decision conferencing Decision engineering Decision making software Decision model Decision ... In 2010, Chevron won the Decision Analysis Society Practice Award for its use of decision analysis in all major decisions. In a ... while descriptive decision-making targets to explain how people actually make decisions, regardless of decision quality), is ...
*  Binary classification
Some of the methods commonly used for binary classification are: Decision trees Random forests Bayesian networks Support vector ... Contexts requiring a decision as to whether or not an item has some qualitative property, some specified characteristic, or ...
*  Grafting (decision trees)
A decision tree is a graphical model that is used as a support tool for decision process. Once the decision tree is constructed ... Pruning allows cutting parts of decision trees to give more clarity and Grafting adds nodes to the decision trees to increase ... Decision Tree Grafting R-tree implementation using branch-grafting method (R-tree implementation) Deep copy and persistence of ... One such decision tree is as follows, Here the X-axis is represented as A and Y-axis as B. There are two cuts in the decision ...
*  Pruning (decision trees)
MDL based decision tree pruning Decision tree pruning using backpropagation neural networks Fast, Bottom-Up Decision Tree ... One of the questions that arises in a decision tree algorithm is the optimal size of the final tree. A tree that is too large ... Pruning is a technique in machine learning that reduces the size of decision trees by removing sections of the tree that ... 269-272 Mansour, Y. (1997), "Pessimistic decision tree pruning based on tree size", Proc. 14th International Conference on ...
*  Decision tree
A decision tree is a decision support tool that uses a tree-like graph or model of decisions and their possible consequences, ... Among decision support tools, decision trees (and influence diagrams) have several advantages. Decision trees: Are simple to ... Decision cycle Decision list Decision table Decision tree model of computation Design rationale DRAKON Markov chain Random ... Decision trees can also be seen as generative models of induction rules from empirical data. An optimal decision tree is then ...
*  Alternating decision tree
An alternating decision tree (ADTree) is a machine learning method for classification. It generalizes decision trees and has ... An alternating decision tree consists of decision nodes and prediction nodes. Decision nodes specify a predicate condition. ... Figure 6 in the original paper demonstrates that ADTrees are typically as robust as boosted decision trees and boosted decision ... Original boosting algorithms typically used either decision stumps or decision trees as weak hypotheses. As an example, ...
*  Incremental decision tree
An incremental decision tree algorithm is an online machine learning algorithm that outputs a decision tree. Many decision tree ... Post-Pruning Incremental Decision Trees. Utgoff, P. E., Berkman, N. C., & Clouse, J. A. (1997) Decision tree induction based on ... Incremental decision tree methods allow an existing tree to be updated using only new individual data instances, without having ... ITI (1997) is an efficient method for incrementally inducing decision trees. The same tree is produced for a dataset regardless ...
*  Decision tree model
Linear decision trees, just like the simple decision trees, make a branching decision based on a set of values as input. As ... The Las Vegas decision-tree complexity R 0 ( f ) {\displaystyle R_{0}(f)} measures the expected depth of a decision tree that ... The quantum decision tree complexity Q 2 ( f ) {\displaystyle Q_{2}(f)} is the depth of the lowest-depth quantum decision tree ... Algebraic decision trees are a generalization of linear decision trees to allow test functions to be polynomials of degree d. ...
*  Decision tree learning
... is the construction of a decision tree from class-labeled training tuples. A decision tree is a flow- ... Decision Trees Tutorial using Microsoft Excel. Decision Trees page at aitopics.org, a page with commented links. Decision tree ... a decision tree can be used to visually and explicitly represent decisions and decision making. In data mining, a decision tree ... Deep Decision Tree , Implementation Evolutionary Learning of Decision Trees in C++ Java implementation of Decision Trees based ...
*  Information gain in decision trees
This biases the decision tree against considering attributes with a large number of distinct values. However, attributes with ... For example, suppose that one is building a decision tree for some data describing the customers of a business. Information ... However, in the context of decision trees, the term is sometimes used synonymously with mutual information, which is the ... but we do not want to include it in the decision tree: deciding how to treat a customer based on their credit card number is ...
*  Oracle Data Mining
ODM offers a choice of well-known machine learning approaches such as Decision Trees, Naive Bayes, Support vector machines, ... Support Vector Machine (SVM). Decision Trees (DT). Anomaly detection. One-class Support Vector Machine (SVM). Regression ...
*  ID3 algorithm
At runtime, this decision tree is used to classify new unseen test cases by working down the decision tree using the values of ... Decision Tree attribute for Root = A. For each possible value, vi, of A, Add a new tree branch below Root, corresponding to the ... In decision tree learning, ID3 (Iterative Dichotomiser 3) is an algorithm invented by Ross Quinlan used to generate a decision ... To avoid overfitting, smaller decision trees should be preferred over larger ones. This algorithm usually produces small trees ...
*  Ross Quinlan
ID3 follows the principle of Occam's razor in attempting to create the smallest decision tree possible. He then expanded upon ... He has contributed extensively to the development of decision tree algorithms, including inventing the canonical C4.5 and ID3 ... ISBN 1-55860-238-0. Quinlan, J. R. (1986). Induction of decision trees. Machine Learning, 1(1):81-106. 2008. (with Qiang Yang, ... The advantages are several orders of magnitude faster, memory efficiency, smaller decision trees, boosting (more accuracy), ...
*  Recursive partitioning
See decision tree. As compared to regression analysis, which creates a formula that health care providers can use to calculate ... Recursive partitioning creates a decision tree that strives to correctly classify members of the population by splitting it ... 1996). "Prospective validation of a decision rule for the use of radiography in acute knee injuries". JAMA. 275 (8): 611-5. doi ... Allows varying prioritizing of misclassifications in order to create a decision rule that has more sensitivity or specificity. ...
*  Digital conversation
The script takes the form of a Decision-Tree and is the backbone of the Digital Conversation. Each Decision-Tree defines two or ... Decision-tree logic >> Behavioural economics A Digital Conversation can be created for any scripted dialogue, thus it is suited ...
*  Photovoltaic power station
"Screening Sites for Solar PV Potential" (PDF). Solar Decision Tree. US Environmental Protection Agency. Retrieved 5 March 2013 ...
*  Deep learning
... by adopting large output layers of the DNN based on context-dependent HMM states constructed by decision trees. Advances in ... "Mastering the game of Go with deep neural networks and tree search". Nature. 529 (7587): 484-489. Bibcode:2016Natur.529..484S. ...
*  Uplift modelling
Typically this would use decision trees or regression analysis. This model would only use the treated customers to build the ... Together with Szymon Jaroszewicz he adapted information theory to build multi class uplift decision trees and published the ... Rzepakowski, Piotr; Jaroszewicz, Szymon (2010). "Decision trees for uplift modeling". In Proceedings of the 10th IEEE ... Rzepakowski, Piotr; Jaroszewicz, Szymon (2011). "Decision trees for uplift modeling with single and multiple treatments". ...
*  Financial economics
See for example: Magee, John F. (1964). "Decision Trees for Decision Making". Harvard Business Review. July 1964: 795-816. ... More traditionally, decision trees-which are complementary-have been used to evaluate projects, by incorporating in the ... Aswath Damodaran (2007). "Probabilistic Approaches: Scenario Analysis, Decision Trees and Simulations". In Strategic Risk ... Edgeworth binomial trees allow for a specified (i.e. non-Gaussian) skew and kurtosis in the spot price; priced here, options ...
*  Tree diagram (probability theory)
Decision tree "Tree Diagrams". BBC GCSE Bitesize. BBC. p. 1,3. Retrieved 25 October 2013. Charles Henry Brase, Corrinne ... In probability theory, a tree diagram may be used to represent a probability space. Tree diagrams may represent a series of ... 205-208 (online copy at Google) tree diagrams - explanations and examples tree diagrams - examples and applications. ...
*  Cascading classifiers
Minguillón, J. (2002). On Cascading Small Decision Trees (PhD thesis). Universitat Autònoma de Barcelona. Zhao, H.; Ram, S. ( ... a decision tree would be: feature 1 negative feature 2 negative feature 3 negative -> class2 feature 3 positive -> class1 ... "Constrained Cascade Generalization of Decision Trees". IEEE Transactions on Knowledge and Data Engineering. 16 (6): 727-739. ... This leads to a tree with too few samples on the leaves. A two-stage algorithm can effectively merge these two cases by giving ...
*  BrownBoost
Dietterich, T. G., (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, ...
*  Mediation
"International Mediation Institute Decision Tree". Retrieved 1 March 2012. Simkin, W. E., (1971); Mediation and the Dynamics of ... Boulle 2005 The International Mediation Institute has a decision tree on its website, which is designed to help the parties to ... Arbiters' decisions are typically final and appeals are rarely successful even if the decision appears to one party to be ... making their own decisions. Those decisions can include settlement agreements or not. Transformative mediation practice is ...
*  Vulnerability index
Decision trees can evaluate alternative policy options. Much of the original research has been evaluated by Lino Briguglio and ...
*  Rule induction
"Generating production rules from decision trees" (PDF). In McDermott, John. Proceedings of the Tenth International Joint ... Some major rule induction paradigms are: Association rule learning algorithms (e.g., Aggrawal) Decision rule algorithms (e.g., ...
*  Riverview Hospital (Coquitlam)
As early as 1967 a decision had been made to downsize Riverview Hospital. The decision was first brought up officially on paper ... Do you think that paint grows on trees? Have you priced a can of pain recently? Do you know the hourly rate for a journeyman ... By 1990 the decision had officially been made to reduce Riverview to a 358-bed facility with the supposed intention of opening ... The report "emphasizes that a full assessment of patients' decision-making abilities and personal support network is necessary ...
*  Multiclass classification
Decision trees are a powerful classification technique. The tree tries to infer a split of the training data based on the ... Several algorithms have been developed based on neural networks, decision trees, k-nearest neighbors, naive Bayes, support ... This strategy requires the base classifiers to produce a real-valued confidence score for its decision, rather than just a ... Hierarchical classification tackles the multi-class classification problem by dividing the output space i.e. into a tree. Each ...