DTREG 是一款完美的建模工具,適用於商業建模或建立多種醫學資料模型,例如性別、人種、婚姻狀況等! 從一組數據值中提取有用信息的過程被稱為「數據挖掘」(data mining)。這些數據可以用來創建模型並做出預測。目前的技術已經發展出許多預測模型,而如何選擇和應用最好的模型則是一種藝術。
DTREG實現強大的已開發預測建模方法。你可以使用決策樹模型、支援向量機(Support Vector Machine, SVM)、基因表示規劃法(Gene Expression Programming)、符號回歸(Symbolic Regression),K-means 分群法(k-means clustering)、判別分析法(discriminant analysis)、線性回歸(Linear Regression models)和(Logistic Regression models)。DTREG也可以進行時間序列分析和預測。
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.
DTREG 提供多種強大的預測建模
- Multilayer Perceptron Neural Networks
- Probabilistic Neural Networks
- General Regression Neural Networks
- RBF Neural Networks
- GMDH Polynomial Neural Networks
- Cascade Correlation Neural Networks
- Support Vector Machine (SVM)
- Gene Expression Programming - Symbolic Regression
- Decision Trees
- TreeBoost — Boosted Decision Trees
- Decision Tree Forests
- K-Means Clustering
- Linear Discriminant Analysis (LDA)
- Linear Regression
- Logistic Regression
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, K-Means 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.
Single-tree, TreeBoost, Decision Tree Forests, Support Vector Machine, K-Means clustering, Linear Discriminant Analysis, Linear Regression and Logistic Regression.
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 a Decision Tree Forest, a Neural Network, a Support Vector Machine, a Gene Expression Programming, a K-Means Clustering, a Linear Discriminant Analysis function a Linear Regression model. or a Logistic Regression model.
Automatic tree pruning.
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.
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.
Standard | Advanced | Enterprise | Enterprise-64 | NET Library (4) | |
---|---|---|---|---|---|
Program address space | 32 bit | 32 bit | 32 bit | 64 bit8 | 32 and 64 bit |
Maximum data rows | 10,000 | 20,000 | (unlimited)7 | (unlimited) | (unlimited) |
Maximum predictor variables | 50 | 100 | (unlimited) | (unlimited) | (unlimited) |
Single tree models | Yes | Yes | Yes | Yes | Yes |
TreeBoost models | Yes | Yes | Yes | Yes | Yes |
Decision Tree Forest models | Yes | Yes | Yes | Yes | Yes |
K-Means clustering models | Yes | Yes | Yes | Yes | Yes |
Discriminant analysis models | Yes | Yes | Yes | Yes | Yes |
Linear regression models | Yes | Yes | Yes | Yes | Yes |
Logistic regression models | Yes | Yes | Yes | Yes | Yes |
Correlation | Yes | Yes | Yes | Yes | N/A |
Factor Analysis | Yes | Yes | Yes | Yes | N/A |
Principal Components Analysis | Yes | Yes | Yes | Yes | N/A |
Multilayer Perceptron Neural Network models | No | Yes | Yes | Yes | Yes |
Probabilistic Neural Network models | No | Yes | Yes | Yes | Yes |
General Regression Neural Network models | No | Yes | Yes | Yes | Yes |
RBF Neural Network models | No | Yes | Yes | Yes | Yes |
GMDH Polynomial Neural Network models | No | Yes | Yes | Yes | Yes |
Cascade Correlation Neural Network models | No | Yes | Yes | Yes | Yes |
Support Vector Machine (SVM) models | No | Yes | Yes | Yes | Yes |
Gene Expression Programming models | No | Yes | Yes | Yes | Yes |
Data transformation language (5) | No | Yes | Yes | Yes | No |
PCA variable transformations | No | No | Yes | Yes | Yes |
Time series analysis and forecasting | No | No | Yes | Yes | No |
Generate scoring code (6) | No | No | Yes | Yes | N/A |
Command-line Operation | No | No | Yes | Yes | N/A |
System Requirements for DTREG:
- Windows 7 -- 11 (There is no version of DTREG for Linux or Macintosh)
- DTREG runs on both 32-bit and 64-bit systems.
- At least 1 GB of memory.
- 20 MB of disk space.