###### SPSS Decision Trees

- IBM SPSS Decision Trees provides classification and decision trees to help you identify groups, discover relationships between groups and predict future events. (ibm.com)
- IBM SPSS Decision Trees requires a valid IBM SPSS Statistics Base license. (ibm.com)

###### regression

- Classification and Regression Trees, Woodsworth International Group (1984). (springer.com)
- You will become familiar with the most successful techniques, which are most widely used in practice, including logistic regression, decision trees and boosting. (coursera.org)
- If you do a web search for 'Classification Trees' or 'Regression Trees' you will get some more complex ideas. (experts-exchange.com)

###### stochastic

- The method, which is called the stochastic decision tree method, is particularly applicable to investments characterized by high uncertainty and requiring a sequence of related decisions to be made over a period of time. (repec.org)
- The stochastic decision tree method builds on concepts used in the risk analysis method and the decision tree method of analyzing investments. (repec.org)
- Stochastic Decision Trees for the Analysis of Investment Decisions ," Management Science , INFORMS, vol. 11(10), pages 244-259, August. (repec.org)

###### optimal

- Máša P., Kočka T. (2006) Finding Optimal Decision Trees. (springer.com)
- This application makes it practicable to evaluate all or nearly all feasible combinations of decisions in the decision tree, taking account of both expected value of return and aversion to risk, thus arriving at an optimal or near optimal set of decisions. (repec.org)
- Are you wanting to find the optimal decision tree? (experts-exchange.com)
- They will not be binary no, and the optimal decision tree is not a issue really. (experts-exchange.com)

###### lesson

- Hello, and welcome to the lesson on Decision Trees. (coursera.org)

###### involves

- Important Precautionary Refinements Decision tree analysis involves cumulating prob- abilities. (jamsadr.com)

###### practice

- Although classification type decision trees has built-in feature selection mechanisms, it is claimed that applying prior feature selection before modelling with decision trees is a useful practice ( Doraisamy et al. (bartleby.com)

###### exploratory

- In addition, this course examines many of the auxiliary uses of trees such as exploratory data analysis, dimension reduction, and missing value imputation. (sas.com)
- use decision trees for exploratory data analysis, dimension reduction, and missing value imputation. (sas.com)

###### binary tree

- You might just need a loop and some ifs, you might need a stack, or you could need a binary tree with a parser. (experts-exchange.com)

###### diagram

- Gain more insight by identifying a particular subset of your data using the tree diagram and running further analysis on this group. (ibm.com)
- However, decision tree will present as a diagram by showing the relationship among possible courses of action, possible events and the potential outcomes for each course of action in the decision (Drury, 2012). (bartleby.com)
- A graphical tool to understand these concepts is introduced here as well, the tree-diagram.Thereafter a number of concepts from set theory are explained and related to probability calculations. (coursera.org)

###### uncertainty

- In this way, decision trees succinctly illustrate the complexity, convolution, and uncertainty that inhabit so much of civil litigation. (jamsadr.com)
- Decision tree analysis Decision tree analysis known as an analytical tool applied to decision-making under condition of uncertainty, also clarifying where there are many possible outcomes for various alternatives and some outcomes are dependent on previous outcomes. (bartleby.com)

###### classify

- Enables you to predict or classify future observations based on a set of decision rules. (ibm.com)

###### Artificial Intelligence

- GATree: Genetically Evolved Decision Trees ", IEEE International Conference on Tools with Artificial Intelligence , Vancouver, Canada, 2000 . (google.com)

###### node

- Save information from trees as new variables such as terminal node number, predicted value and predicted probabilities. (ibm.com)
- Information gain is the procedure to select a particular attribute to be a decision node of a decision tree. (codeproject.com)
- Decision trees are read from the top down, starting at the root node. (coursera.org)

###### Model

- Create a non-linear model using decision trees. (coursera.org)
- When the response variable is categorical, the model is called a classification tree. (coursera.org)
- A User-Oriented Model For Incorporating Risk Into Short-Run Decisions ," Southern Journal of Agricultural Economics , Southern Agricultural Economics Association, vol. 9(02), December. (repec.org)

###### empirical

- It permits the use of subjective probability estimates or empirical frequency distributions for some or all factors affecting the decision. (repec.org)

###### module

- In this module, you will become familiar with the core decision trees representation. (coursera.org)
- In this module, through various visualizations and investigations, you will investigate why decision trees suffer from significant overfitting problems. (coursera.org)

###### Create

- Decision trees create segmentations or subgroups in the data, by applying a series of simple rules or criteria over and over again, which choose variable constellations that best predict the target variable. (coursera.org)
- used to create a decision tree for classification purposes. (coursera.org)

###### Machine Learning

- Breeding Decision Trees Using EvolutionaryTechniques ", International Conference on Machine Learning, Williamstown, Massachusetts, June-July 2001 . (google.com)
- supervised machine learning techniques such as decision trees. (coursera.org)
- Out of all machine learning techniques, decision trees are amongst the most prone to overfitting. (coursera.org)

###### depends

- The performance of a DT in a complex decision problem depends on the efficiency of its construction. (inria.fr)

###### fundamental

- Attribute selection is the fundamental step to construct a decision tree. (codeproject.com)

###### complex

- In an optional segment, you will design a very practical approach that learns an overly-complex tree, and then simplifies it with pruning. (coursera.org)

###### made

- are many decisions which have to be made. (bartleby.com)
- The discussion will include the major factors involved in making the decision and also show how the final decision was made. (bartleby.com)
- why each decision was made as you traverse the tree. (coursera.org)
- Here the relation is made to tree-diagrams again, as well as contingency tables. (coursera.org)

###### Describe

- Describe the underlying decision boundaries. (coursera.org)

###### Software

- Bravo M.C. (2000) Strata Decision Tree SDA Software. (springer.com)

###### Improve

- To obtain a 2- to 3-fold increase in speed (admittedly, with a raw implementation, so we might improve there a bit as well) we are sacrificing some space, O ( n ), where n is the training set size, to store training set data in the decision tree leaves. (google.com)

###### outcome

- A decision tree uses a tree structure to represent a number of possible decision paths and an outcome for each path. (oreilly.com)

###### Variable

- throughout the tree, as variable B does here. (coursera.org)

###### tool

- Basically I am using it as a forecasting tool, where based on a set of data with 20 columns and say 1 million rows a decision tree is built where it points to a specific column as the output. (experts-exchange.com)

###### help

- 1 By exposing these in a graphic presentation, decision trees also help clients and law- yers understand the succession of and the dynamics between the pivot points. (jamsadr.com)
- Decision trees' numbers can help clients feel that their settlement decisions (yea or nay) are not undisciplined or arbitrary but supported by a process that provides logic and reasoning. (jamsadr.com)