Publisher's description
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.
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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