
DTREG


DTREG is the ideal tool for modeling business and
medical data with categorical variables such as sex, race and marital status.



Decision trees present a clear, logical model
that can be understood easily by people who are not mathematically inclined.



If you have a need for linear or nonlinear regression
analysis, check out the NLREG program.



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DTREG
Software For Predictive Modeling and Forecasting
DTREG offers powerful predictive modeling methods:


DTREG also can perform time series analysis and forecasting.
DTREG includes Correlation, Factor Analysis, Principal Components Analysis, and PCA Transformations of variables
The process of extracting useful information from a set of data values is
called “data mining”. This data can be used to create
models to make predictions.
Many techniques have been developed for predictive modeling,
and there is an art to selecting and applying
the best method for a particular situation.
DTREG implements the most powerful predictive
modeling methods that have been developed indlucing,
TreeBoost and
Decision Tree Forests as well as
Neural Networks,
Support Vector Machine,
Gene Expression Programming and Symbolic Regression,
KMeans Clustering,
Linear Discriminant Analysis,
Linear Regression models and
Logistic Regression models.
Benchmarks have shown these methods to be
highly effective for analyzing and modeling many types of data.
DTREG Training Tutorials
DTREG Features
 Ease of use. DTREG is a robust application that is installed easily on any Windows system.
DTREG reads Comma Separated Value (CSV) data files that are easily created from almost any data source.
Once you create your data file, just feed it into DTREG, and let DTREG do all of the work of creating
a decision tree, Support Vector Machine, KMeans clustering,
Linear Discriminant Function, Linear Regression or Logistic Regression model.
Even complex analyses can be set up in minutes.
 Classification and Regression Trees. DTREG can build Classification Trees where the
target variable being predicted is categorical and Regression Trees where the target variable is
continuous like income or sales volume.
 Singletree, TreeBoost, Decision Tree Forests, Support Vector Machine,
KMeans clustering, Linear Discriminant Analysis, Linear Regression and
Logistic Regression.
By simply checking a button, you can
direct DTREG to build a classic singletree model, a
TreeBoost model consisting of a series of trees
a Decision Tree Forest,
a Neural Network,
a Support Vector Machine,
a Gene Expression Programming,
a KMeans Clustering,
a Linear Discriminant Analysis function
a Linear Regression model.
or a Logistic Regression model.
 Automatic tree pruning. DTREG uses Vfold crossvalidation to determine the optimal tree
size. This procedure avoids the problem of "overfitting" where the generated tree fits the training
data well but does not provide accurate predictions of new data.
 Surrogate variables for missing data.
DTREG uses a sophisticated technique involving
"surrogate variables" to handle cases
with missing values. This allows cases with some available
values and some missing values to be utilized to the maximum extent when building the model.
It also enables DTREG to predict the values of cases that have missing values.
 Visual display of the tree. DTREG can display the generated decision tree on the screen,
write it to a .jpg or .png disk file or print it. When printed, DTREG uses a sophisticated technique
for paginating trees that cross multiple pages.
 DTREG accepts text data as well as numeric data.
If you have categorical variables with data values such as “Male”, “Female”, “Married”,
“Protestant”, etc., there is no need to code them as numeric values.
 Data Transformation Language (DTL). DTREG includes a full
Data Transformation Language (DTL) programming language for
transforming variables, creating new variables and selecting which cases are to be
included in the analysis.
 Project files for saving analyses. DTREG saves all of the information about variables,
analysis parameters as well as the generated report and tree in a project file. You can later
open the project file, alter parameters or rerun it with a different dataset.
 Scoring to predict values. Once a decision tree has been built, you can use DTREG to
"score" a new dataset and predict values for the target variable.
 Generated scoring source code. The "Translate" function in DTREG
generates C, C++ and SAS® source code to compute predicted values. This source
code can be included in application programs to perform high performance scoring
of large volumes of data.
 Heavy duty capability. The Enterprise Version of DTREG can
handle an unlimited number of
data rows  hundreds of thousands or millions are no problem. DTREG can build classification trees
with predictor variables that have hundreds of categories by using an efficient clustering algorithm.
Many other decision tree programs limit predictor variables to 16 or less categories.
 DTREG .NET Class Library. The DTREG .NET Class Library can be called
from application programs to generate models and compute predicted target values using a model generated by
DTREG.
Roadmap to Understanding and Building Predictive Models
Download demonstration copy
of DTREG.
Download manual for DTREG.
Download manual for DTREG .NET Class Library.
Order DTREG.
Google Scholar search for published articles citing DTREG.

The author of DTREG is available for consulting on data modeling and
data mining projects.
Contact via email for information.

