MARSplines For Classification With Deployment

Full-featured implementation of Multivariate Adaptive Regression Splines (MARSplines) for classification problems; with automatic deployment.

General

Element Name Description
Detail of computed results reported Detail of computed results; if Minimal detail is requested, spreadsheets of model summary, model coefficients, classification statistics (confusion matrix) and descriptive statistics will be displayed; at the Comprehensive level of detail, a spreadsheet of predictions and accuracy as well as their histogram plots will be displayed; in addition to the above, the All results level will display a spreadsheet (if the 'Creates residual statistics' option is selected) containing all data set variables and their statistics including predictions and accuracy.
Missing data deletion Specifies the substitution method for missing data. Casewise excludes cases that contain any missing data for any of the selected variables in the analysis. Mean substitution replaces missing data by the means for the respective variables (Note: This option is not applicable for categorical dependent and predictor variables).
Generate datasource, if N for input less than Generate a data source for further analyses with other Data Miner nodes if the input data source has fewer than k observations, as specified in this edit field; note that parameter k (number of observations) will be evaluated against the number of observations in the input data source, not the number of valid or selected observations.

Options

Element Name Description
Maximum number of basis functions Specifies the maximum number of basis functions the model can have. The larger this number the more flexible (complex) the resulting model will be.
Degree of interactions Specifies the degree of interactions between the variables.
Penalty for adding basis functions Specifies the penalty (cost) for selecting each basis function. The larger this smoothing parameter is the fewer basis functions are selected.
Threshold Specifies a stopping threshold to prevent overfitting.
Apply pruning Apply pruning to control model complexity.
Apply memory limit Use this option to limit the maximum data size that can be processed; note that very large data problems may require significant memory and processing resources; modify the defaults only as needed.
Memory limit Use this option to set the maximum data size that can be processed.
Creates residual statistics Creates predicted and residual statistics for each case depending on the selected level of details.
Draw scatter plots Draws the scatter plots of the independent variables selected by MARSplines versus the dependent variables (observed and predicted values).

Results

Element Name Description
Include inputs Includes the independent variables in spreadsheets and histograms.
Include outputs Includes the dependent variables in spreadsheets and histograms.
Include predictions Includes predictions in spreadsheets and histograms.
Include classification accuracy Includes classification accuracy in spreadsheets and histograms.
Include classification confidence levels Includes confidence levels for classification in spreadsheets and histograms.

Deployment

Deployment is available if the Statistica installation is licensed for this feature.

Element Name Description
Generates C/C++ code Generates C/C++ code for deployment of predictive model.
Generates SVB code Generates Statistica Visual Basic code for deployment of predictive model.
Generates PMML code Generates PMML (Predictive Models Markup Language) code for deployment of predictive model. This code can be used via the Rapid Deployment options to efficiently compute predictions for (score) large data sets.