2215 questions found.

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The 'computedVarsProgram' subparameter (alias: 'compPgm') specifies the expression for each computed variable defined in the 'computedVars' parameter within 'modelTable'....
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The 'dataSourceOptions' subparameter (aliases: 'options', 'dataSource') within 'modelTable' is used to specify various data source options for the input model table....
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The 'importOptions' subparameter (alias: 'import') in 'modelTable' specifies the settings for reading the model table from its data source....
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The 'name' subparameter within 'modelTable' is a required parameter that specifies the actual name of the input model table....
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The 'singlePass' subparameter (default: FALSE) in 'modelTable' controls whether a transient table is created on the server. If TRUE, no transient table is created, which can be efficient but might lead to unstable data ordering on repeated runs....
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The 'vars' subparameter within 'modelTable' specifies the particular variables from the model table that should be used in the 'forestCode' action....
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The 'where' subparameter within 'modelTable' specifies an expression to subset the input data from the model table....
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The 'whereTable' subparameter in 'modelTable' specifies an input table whose rows are used as a WHERE filter for the model data. If the 'vars' subparameter is not specified for 'whereTable', all common variable names between the input table and the filtering table are used for matching rows. This fi...
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The 'nTree' parameter (alias: 'nTrees', default: MACINT, minimum: 1) specifies the number of trees from the forest model to use when generating scoring code....
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The 'vote' parameter (default: MAJORITY) specifies the vote strategy for classification, influencing both the generated SAS scoring code and out-of-bag (OOB) error computations....
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The 'MAJORITY' option for the 'vote' parameter indicates that the prediction should be based on the majority vote among the trees in the forest....
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The 'PROB' option for the 'vote' parameter indicates that the prediction should be based on the average probability calculated from all trees in the forest....
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The forestScore action is used to score a table using a forest model....
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The 'casOut' parameter specifies the table where the scored results will be stored. If this parameter is not specified, the action only computes the mis-classification rate for classifications and the mean squared error for regressions....
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When the 'assess' parameter is set to True, predicted probabilities are added to the result table for the event levels. These can be used with the assess action....
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Setting 'assessOneRow' to True adds predicted probabilities to the result table, where all event probabilities are included as separate columns named with the prefix "_DT_P_"....
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The 'copyVars' parameter allows you to specify a list of variables to transfer from the input table to the output table....
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The 'impute' parameter specifies how to handle observations that have a non-missing value for the target variable. When set to True, the observed values are assumed to be known without error and are used as the predicted values....
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By default, the 'includeMissing' parameter is True, so observations with missing values are included. If set to False, observations with missing values for the variables used in the tree model are ignored....
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The 'modelTable' parameter is required to specify the table containing the model....
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The 'vote' parameter specifies the vote strategy for classification. You can choose "MAJORITY" to use the majority vote or "PROB" to use the average probability from the forest....
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You can set the 'treeVotes' parameter to True to enhance the scored table with information about the votes of the individual trees....
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The 'varIntImp' parameter requests variable interaction importance and specifies the maximum degree of interaction, with a range of 0 to 3....
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You can specify a value greater than zero for the 'seed' parameter. By default, the seed is 0, which bases the random number stream on the computer clock....
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specifies the value to use for minimal cost-complexity pruning for regression trees.
Minimum value: 0...
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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

format: specifies the format to apply to the variable.
formattedLength: spe...
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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 fraction of the data for the bootstrap sample.
Default: 0.63212055882
Range: (0–1]...
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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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specifies the aggressiveness of tree pruning according to the C4.5 algorithm.
Default: 0.25...
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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.

casOut: specifies the settings for an output table.
For more information about specifying the casOut parameter, see the common ...
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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 speci...
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specifies the split criterion for each tree node....
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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 event values of the target variable. This parameter is combined with the eventFreq parameter to specify the frequency for each specific event. Observations with the specified event are reweighted with the value from the eventFreq parameter. Specifying this parameter is useful for rare-...
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specifies the frequency for each corresponding event in the event parameter....
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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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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 input variables to use in the analysis.
For more information about specifying the inputs parameter, see the common casinvardesc parameter.
Alias: input

format: specifies the format to apply to the variable.
formattedLength: specifies the length of the format field plus the length of t...
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specifies training an isolation forest.
Default: FALSE...
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specifies the minimum number of observations on each node.
Default: 5
Minimum value: 1...
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specifies number of variables to split with using the LOH method.
Default: 0...
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specifies the number of input variables to consider for splitting on a node. The variables are selected at random from the input variables for each tree. By default, forest uses the square root of the number of input variables is used, rounded up to the nearest integer. For gradient boosting, the nu...
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specifies the maximum number of children (branches) allowed for each level of the tree.
Default: 2
Minimum value: 1...
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specifies the maximum number of the tree level.
Default: 6
Minimum value: 1...
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by default, when the largest value in one bin matches the lowest value in a neighboring bin, the values are merged into the lower bin. When set to False, the action does not try to merge bins.
Default: TRUE...
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specifies a threshold for utilizing missing values in the split search when the missing parameter is set to USEINSEARCH. If the number of observations in which the splitting variable has missing values in a node is greater than or equal to the specified value, then the action initiates the USEINSEAR...
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specifies the missing policy to handle missing values.
Default: USEINSEARCH
MACSMALL: specifies to treat the missing values for numeric variables as the smallest machine value and to treat missing values for nominal variables as a separate level.
USEINSEARCH: specifies to incorporate missing values ...
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