K-Nearest Neighbors with Deployment (Regression)

Full-featured implementation of K-Nearest Neighbors (KNN) for multiple regression problems. The final solution is automatically stored for deployment.

General

Element Name Description
Detail of computed results reported Detail of computed results; if Minimal detail is requested, spreadsheets of analysis summary, model specifications as well descriptive statistics (regression statistics) will be displayed; at the Comprehensive level of detail, a spreadsheet of predictions and residuals 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 residuals (whichever applicable).
Missing data deletion.
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.

Sampling

Element Name Description
Sampling method Sampling method to be used for dividing the data set into example and test subsets. Random sampling will divide the dataset into example and testing samples in a random fashion. This is in contrast to the First N method which selects the first N cases as the training set and the rest as the testing sample. NOTE: you may also use a learning/testing indicator variable method for sampling from the data. You can access this functionality via the Advanced tab of the data spreadsheet in the Data Acquisition of Statistica Data Miner environment. Selecting this method (i.e. learning/testing indicator) will override any choice of sampling you make on this tab.
Size of example set (%) Specifies the percentage of data cases that will be used as examples. The remaining valid cases in the dataset will be used to form the test sample.
Seed Specifies the random generator seed for dividing data into the example and test sets
Use first N cases Selects the first N valid cases in the data set as training subset. The rest are used for testing

Options

Element Name Description
Number of nearest neighbors
Distance measure Specifies the metric to be used for measuring the distance between two points in the input space.
Standardize distances Select this option to standardize distances.
Use weighted average\voting for predictions.

Cross-validation

Element Name Description
Apply v-fold cross-validation Applies v-fold cross-validation to obtain estimates of the capacity, epsilon and nu parameters
V value Number of cross-validation folds
Seed Seed value for random data shuffling for cross-validation
Minimum K Start value for number of nearest neighbors (used by cross-validation grid search)
Maximum K End value for number of nearest neighbors (used by cross-validation grid search)
Increment in K

Memory usage

Element Name Description
Restrict memory usage Restrict the amount of memory that can be used by the analysis.
Amount of memory that can be used by the analysis Amount of memory that can be used by the analysis.

Results

Element Name Description
Subset used to generate results
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 residuals Includes residuals in spreadsheets and histograms.
Include standard deviations Includes standard deviations for regression predictions in spreadsheets and histograms (meaningful only when number of nearest neighbors is larger than one).
Creates residual statistics Creates predicted and residual statistics for each case depending on the selected level of details.

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.
Saves C/C++ code Save C/C++ code for deployment of predictive model.
File name for C/C code Specify the name and location of the file where to save the (C/C++) deployment code information.
Saves SVB code Save Statistica Visual Basic code for deployment of predictive model.
File name for SVB code Specify the name and location of the file where to save the (SVB/VB) deployment code information.
Saves PMML code Saves 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
File name for PMML (XML) code Specify the name and location of the file where to save the (PMML/XML) deployment code information.