DTREG is a powerful statistical analysis program that
generates classification and regression decision trees
that model data and can be used to predict values.

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 and pruning it to
the optimal size. Even complex analyses can be set up
in minutes.

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.

By simply checking a button, you can direct DTREG to
build a classic single-tree model, a TreeBoost model
consisting of a series of trees or a Decision Tree
Forest model.

DTREG uses V-fold cross-validation 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.

DTREG uses a sophisticated technique
involving "surrogate splitters" 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.

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 pagenating trees that cross multiple pages.

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