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Use the 'nLevelsPrint' parameter to specify the maximum number of variable levels to display. Setting it to 0 suppresses all levels....
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Yes, the 'groupByLimit' parameter can be used to suppress the analysis if the number of BY groups exceeds the specified integer value....
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The frontierCost action is designed for analyzing stochastic frontier cost models....
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You use the "table" parameter to specify the input table containing the data to be analyzed....
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You define the model using the "model" parameter. Within this parameter, you specify the dependent variable(s) using "depVars" and the explanatory effects using the "effects" parameter....
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The action supports three types of frontier models, specified via the "type" subparameter in "modelOptions": "EXPONENTIAL" (the default), "HALF" (half-normal), and "TRUNCATED" (truncated-normal)....
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You can request technical efficiency estimates using the "output" parameter. Specifically, use the "te1" and "te2" subparameters to specify the variable names for technical efficiency type 1 and type 2, respectively....
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Yes, you can use the "tests" parameter to specify linear hypotheses about the regression parameters. The action supports Wald, Lagrange multiplier (LM), and likelihood ratio (LR) tests....
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You can specify classification variables using the "class" parameter. This parameter allows you to define options such as the parameterization method ("param"), the reference level ("ref"), and the sort order ("order") for the categorical variables....
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The "optimizer" parameter's "algorithm" option allows you to choose from several techniques, including "CONJUGATEGRADIENT", "DOUBLEDOGLEG", "NEWTONRAPHSONWITHLINESEARCH" (the default), "NEWTONRAPHSONWITHRIDGING", "QUASINEWTON", "TRUSTREGION", or "NONE"....
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You can use the "outputTables" parameter to list the specific display tables you wish to save as CAS tables using the "names" subparameter....
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Yes, the "initialvalues" parameter allows you to specify a list of initial values for the parameters to start the optimization process....
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The gbtreeCode action is designed to generate DATA step scoring code from a gradient boosting tree model....
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The 'modelTable' parameter is required to specify the table containing the gradient boosting tree model....
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The 'encodeName' parameter specifies whether to encode variable names, such as predicted probabilities for binary or nominal targets. If set to TRUE, variables use the prefix 'P_' instead of the default '_DT_P_'. The default value is FALSE....
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Setting 'singlePass' to TRUE prevents the creation of a transient table on the server, which can be more efficient. However, it may result in data not having a stable ordering upon repeated runs....
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Computed variables can be defined using the 'computedVars' and 'computedVarsProgram' parameters nested within 'modelTable'. Additionally, setting 'computedOnDemand' to TRUE creates these variables when the table is loaded rather than when the action begins....
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You must specify the 'code' parameter to request that the action produce SAS score code. This parameter accepts additional subparameters for customization....
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Scores a table using a gradient boosting tree model....
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Specifies that you wish the action use a prespecified row ordering. This requires using the orderby and groupby parameters on a preliminary table.partition action call. Alias: reproducibleRowOrder. Default: FALSE....
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when set to True, predicted probabilities are added to the result table for the event levels. You can use these predicted probabilities with the assess action. Default: FALSE....
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when set to True, predicted probabilities are added to the result table for the event levels. All event probabilities are included as separate columns and are named with the prefix _DT_P_. You can use these predicted probabilities with the assess action. Default: FALSE....
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specifies the table to store the scored results in. When not specified, the action scores the data set and computes only the mis-classification rate for classifications and mean squared error for regressions. For more information about specifying the casOut parameter, see the common casouttable para...
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specifies the variables to transfer from the input table to the output table. Alias: copyVar....
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specifies whether to encode the variable names such as predicted probabilities of a binary or nominal target in the generated casout table. The predicted probabilities are named with the prefix P_ instead of _DT_P_. Default: FALSE....
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by default, observations with missing values are included. When set to False, observations with missing values for the variables used in the tree model are ignored when scoring. Default: TRUE....
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specifies the model ID variable name to use when generating the scored table. By default, the variable name is _DT_PredName_ for classifications, _DT_PredLowerbd_ and _DT_PredUpperbd_ for a binned target, and _DT_PredMean_ for regressions....
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specifies the table containing the model. Long form: modelTable={name="table-name"}. Shortcut form: modelTable="table-name". Alias: model. The castable value can be one or more of the following: caslib="string". specifies the caslib for the input table that you want to use with the action. By defaul...
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specifies the number of trees to use while scoring. Alias: nTrees. Default: MACINT. Minimum value: 1....
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specifies variable importance using the random branch assignments (RBA) method. Default: FALSE....
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specifies the seed for the random number generator. By default, the random number stream is based on the computer clock. Negative values also result in random number streams based on the computer clock. If you want a reproducible random number sequence between runs, specify a value that is greater t...
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specifies the settings for an input table. Long form: table={name="table-name"}. Shortcut form: table="table-name". The castable value can be one or more of the following: caslib="string". specifies the caslib for the input table that you want to use with the action. By default, the active caslib is...
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specifies the target variable when scoring a data set. If the target variable name in the tree model is the same in the scored table, then this option is not required....
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requests that the scored table generated by scoring forest is enhanced with information about the votes of the individual trees. Default: FALSE....
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requests variable interaction importance and specifies the maximum degree of interaction. Default: 1. Range: 0–3....
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Trains a gradient boosting tree. This action requires a SAS Visual Data Mining and Machine Learning license....
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Specifies that you wish the action use a prespecified row ordering. This requires using the orderby and groupby parameters on a preliminary table.partition action call. Alias: reproducibleRowOrder. Default: FALSE....
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specifies temporary attributes, such as a format, to apply to input variables. For more information about specifying the attributes parameter, see the common casinvardesc parameter. Aliases: attribute, attrs, attr, varAttrs....
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specifies a variable for transfer learning that indicates which observations are from an auxiliary source. A value of 0 indicates a traditional training observation. Other values indicate auxiliary data....
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by default, the bin order is preserved for numeric variables. When set to False, the bin order is ignored for numeric variables. Default: TRUE....
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specifies the table to store the decision tree model in. When not specified, a random name is generated. For more information about specifying the casOut parameter, see the common casouttable parameter....
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requests that the action produce SAS score code. Specify additional parameters. For more information about specifying the code parameter, see the common codegen parameter....
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requests that the action produce SAS score code to create variables encoding interactions. You must also request variable interactions of at least degree 2. The viicodegen value can be one or more of the following: casOut: specifies the settings for an output table. For more information about specif...
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specifies the distribution in gradient boosting tree. Default: BINARY. BINARY: this value is useful for binary classification. GAUSSIAN: this value is useful for regression trees. MULTINOMIAL: this value is useful for multinomial distribution for classification with more than two classes. POISSON: t...
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The tkcasdt_earlyStop value can be one or more of the following: metric: 'ASE' | 'LOGLOSS' | 'MCR'. minimum: Default: FALSE. stagnation: Default: 0. Minimum value: 0. threshold: Default: 0. Minimum value: 0. thresholdIter: Default: 0. Minimum value: 0. tolerance: Default: 0. Minimum value: 0....
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specifies whether to encode the variable names such as predicted probabilities of a binary or nominal target in the generated casout table. The predicted probabilities are named with the prefix P_ instead of _DT_P_. Default: FALSE....
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specifies the FCMP evaluation metric for gradient boosting tree models. Alias: fcmpEvalFunc....
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specifies a numeric variable that contains the frequency of occurrence of each observation....
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by default, a greedy search or exhaustive search is used to determine the best split for each variable of each tree node. When set to False, a fast and efficient algorithm that is based on clustering is applied. Setting this parameter to False is recommended for variables with high cardinality. Defa...
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