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Decision tree learning
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271	3. From decision trees to decision forest. A decision forest is a set of several decision trees. These trees can be formed by various methods (or by one method, but with various parameters of work), by different sub-samp Add to Reading List Source URL: www.math.nsc.ru Language: English - Date: 2004-10-12 13:38:54 Computational statistics Decision trees Bootstrapping Data analysis Resampling Decision tree learning Sampling Bootstrap aggregating Quantitative comparative linguistics Statistics Machine learning Statistical inference
272	2. How to build decision trees? The procedure of the formation of a decision tree by statistical data is also called construction of a tree. In this paragraph we will get acquainted to some ways of construction of trees Add to Reading List Source URL: www.math.nsc.ru Language: English - Date: 2004-10-12 13:38:54 Data analysis Econometrics Regression analysis Decision tree learning Sample size determination Decision tree model Variance Supervised learning Statistics Decision trees Machine learning
273	2.8 Quality estimation and methods comparison. During quality estimation of the method of decision tree constructions, it is necessary to take into account required computer resources (time and memory). At the same time, Add to Reading List Source URL: www.math.nsc.ru Language: English - Date: 2004-10-12 13:38:54 Machine learning Estimation theory Signal processing Sample size determination Cross-validation Sample Quantitative comparative linguistics Estimating equations Statistics Sampling Statistical inference
274	Methods for statistical data analysis with decision trees Problems of the multivariate statistical analysis In realizing the statistical analysis, first of all it is necessary to define which objects and for what purpose Add to Reading List Source URL: www.math.nsc.ru Language: English - Date: 2004-10-12 13:38:54 Statistical inference Decision trees Data mining Decision tree learning Binary tree Statistical classification Regression analysis Supervised learning Linear discriminant analysis Statistics Machine learning Econometrics
275	Extracting Knowledge and Computable Models from Data - Needs, Expectations, and Experience ¨ Thomas Natschlager, Felix Kossak, and Mario Drobics Software Competence Center Hagenberg, A-4232 Hagenberg, Austria Add to Reading List Source URL: www.unisoftwareplus.com Language: English - Date: 2008-03-03 05:12:56 Machine learning Data mining Data analysis Formal sciences Computational neuroscience Pattern recognition Neural network Decision tree learning Mathematica Statistics Science Mathematical software
276	5. Software description for decision tree construction. Now, dozens of computer programs for construction of decision trees are known. The difference between these programs lies into in a type of solved problems, in used Add to Reading List Source URL: www.math.nsc.ru Language: English - Date: 2004-10-12 13:38:54 Statistical methods Decision trees Regression analysis Decision tree learning Forecasting Subroutine Predictive analytics Linear regression Statistics Econometrics Machine learning
277	Unit 4 DECISION ANALYSIS Lesson 37 Learning objectives: • Add to Reading List Source URL: businessmanagementcourses.org Language: English - Date: 2007-06-08 11:40:09 Science Knowledge Decision tree Influence diagram Decision analysis Decision making EMV Pruning Decision theory Decision trees Statistics
278	Classification Trees With Unbiased Multiway Splits Hyunjoong Kim and Wei-Yin Loh∗ (J. Amer. Statist. Assoc., 2001, 96, 598–604) Abstract Add to Reading List Source URL: www.stat.wisc.edu Language: English - Date: 2008-08-25 23:11:22 Regression analysis Data mining Decision tree learning Econometrics Feature selection CHAID Linear discriminant analysis B-tree Contingency table Statistics Decision trees Machine learning
279	Classification Trees with Bivariate Linear Discriminant Node Models Journal of Computational and Graphical Statistics, 2003, 12, 512–530 Wei-Yin Loh ∗ Department of Statistics University of Wisconsin Add to Reading List Source URL: www.stat.wisc.edu Language: English - Date: 2005-02-17 17:32:54 Binary trees Multivariate statistics Machine learning Linear discriminant analysis Decision tree learning T-tree Tree Pruning Logistic regression Statistics Decision trees Statistical classification
280	Department of Statistics University of Wisconsin, Madison Technical Report 989 March 3, 1998 (revised October 21, 2008) CRUISE User Manual Add to Reading List Source URL: www.stat.wisc.edu Language: English - Date: 2008-10-21 22:05:03 Model selection Data analysis Statistical inference Linear discriminant analysis Decision tree learning Feature selection Cross-validation Pruning Missing data Statistics Machine learning Decision trees