Models, Radial Basis Function (RBF) neural networks, self-organizingĬascade Correlation neural networks, Probabilistic neural networksĤ) Automated architecture. Networks have the theoretical capability of modeling any type of function.Ģ) Accuracy - Probabilistic neural networks are extremely accurate andģ) DTREG variety - DTREG supports 3- and 4-layer perceptron network To a wide variety of classification and regression problems. A table ranking overall variable importance isġ) Wide applicability - Neural networks have been successfully applied DTREG carries this further byĪnalyzing all of the splits generated by each variable and the selection Variables are used to split nodes near the top of the tree, you can quicklyĭetermine the most important variables. Produce the most accurate results of any modeling methods.ħ) Decision trees identify important variables - By examining which For many applications these "ensemble" tree methods Interactions by partitioning the cases and then analyzing each groupĦ) Highly accurate "ensemble" tree models - DTREG providesĬlassical, single-tree models and also TreeBoost and Decision Treeįorest models. Decision trees automatically deal with these There may be significant differences between men/women, people Malignant/benign, frequent buyer/occasional buyer, etc.ĥ) Decision trees automatically handle interactions between variables. Value but can be a predicted category such as male/female, The predicted value from a decision tree is Not simply a numerical In contrast, categorical variables areĤ) Decision trees can perform classification as well as regression. Geographic region are difficult to model using numerically-oriented They can be understoodĪnd used by people who are Not mathematically gifted.ģ) Decision trees handle both continuous and categorical variables -Ĭategorical variables such as gender, race, religion, marital status and Machine (SVM), gene expression programming, linear discriminantĢ) Decision trees are easy to understand - Decision trees provide aĬlear, logical representation of the data model. DTREG implements the mostĪdvanced 'predictive modeling' methods that have been developedĭTREG features/capabilities of Decision Tree Based Models:ġ) Decision trees are easy to build - Just feed a dataset into DTREG,Īnd it will do all the work of building a decision tree, support vector Predictive modeling, and there is an art to selecting and applying theīest method for a particular situation. Note: The process of extracting useful information from a set of data Medical data with categorical variables such as sex, race and marital DTREG is the ideal tool for modeling business and Regression) Linear Discriminant Analysis (LDA) and Logistic Machines (SVM) Gene Expression Programming (Symbolic (PNN) General Regression Neural Networks (GRNN) Support Vector Networks (“self organizing” networks) Probabilistic Neural Networks (Ensemble of Trees) Multilayer Perceptron Neural Networks Radialīasis Function (RBF) Neural Networks Cascade Correlation Neural Trees (Classification and Regression Trees) TreeBoost - Boostedĭecision Trees (Stochastic Gradient Boosting) Decision Tree Forests Offers the most advanced 'predictive modeling' methods: Decision Category Intelligent Software>Neural Network Systems/Tools, Intelligent Software>Data Mining Systems/Tools and Intelligent Software>Gene Expression Programming Systems/ToolsĪbstract DTREG is a decision tree building software product that canīe used for Predictive Modeling (Data Mining) and Forecasting.
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