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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....
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specifies the L1 norm regularization on prediction. The value must be greater than or equal to zero. Default: 0. Minimum value: 0....
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specifies the minimum number of observations on each node. Default: 5. Minimum value: 1....
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specifies the learning rate of each tree. Default: 0.1. Range: (0–1]....
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Default: 0. Minimum value: 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: 5. 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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Default: 0. Minimum value: 0....
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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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specifies the model ID variable name to use when generating SAS score code. By default, DT_ is prefixed to the target variable name....
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specifies the table containing the model. For more information about specifying the modelTable parameter, see the common castable parameter....
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specifies interval inputs whose prediction should not increase when the input value increases. Perfect compliance is not guaranteed. Aliases: monotoneDecrease, monotoneDec, Dec....
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specifies interval inputs whose prediction should not decrease when the input value increases. Perfect compliance is not guaranteed. Aliases: monotoneIncrease, monotoneInc, Inc....
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specifies the number of bins to use for numeric variables in the calculation of the decision tree. Default: 50. Minimum value: 1....
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specifies the nominal input variables to use in the analysis. For more information about specifying the nominals parameter, see the common casinvardesc parameter. Alias: nominal....
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specifies the method for finding a split on a nominal input. Alias: nomSearch. The tkcasdt_nomSearchOpts value can be one or more of the following: handling: "CLASSIC" | "ENHANCED". maxCategories: specifies the maximum number of levels for a splitting rule to include. Aliases: maxCats, maxLevels, ma...
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specifies the number of trees to create. Alias: nTrees. Default: 50. Minimum value: 1....
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specifies an offset variable to use with distribution=POISSON or TWEEDIE....
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this value is useful for the power parameter in tweedie distribution. Alias: scale. Minimum value (exclusive): 0....
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this value is useful for the power parameter in tweedie distribution. Default: 1.5. Range: (1, 2)....
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specifies bin boundaries at quantiles of numerical inputs instead of bins of equal width. Aliases: qbin, qtbin. Default: TRUE....
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specifies the L2 norm regularization on prediction. The value must be greater than or equal to zero. Default: 1. Minimum value: 0....
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specifies the table to store the generated aStore model. For more information about specifying the saveState parameter, see the common casouttable parameter....
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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 a small value to avoid zero in division. Default: 1E-12. Minimum value: 0....
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specifies the fraction of the data to use for building each tree. Aliases: subsample, samplingRate. Default: 0.5. Range: (0–1]....
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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: specifies the caslib for the input table that you want to use with the action. By default, the active caslib is used. Sp...
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specifies the target or response variable for training. If the variable is numeric, but not specified in the nominal= parameter and nbinstarget= is not specified, then a regression tree is trained....
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during transfer learning specifies the number of trees to create before down-weighting of auxiliary observations begins. Default: 0. Minimum value: 0....
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during transfer learning specifies how much to down-weight unproductive auxiliary data. Default: 0.9. Range: 0–1....
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during transfer learning specifies the fraction of the distribution of gradients on the training data beyond which auxiliary observations are down-weighted. Default: 0.01. Range: (0–0.5]....
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specifies the settings for an input table. Long form: validTable={name="table-name"}. Shortcut form: validTable="table-name". The castable value can be one or more of the following: caslib: specifies the caslib for the input table that you want to use with the action. By default, the active caslib i...
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specifies whether the variable importance information is generated. The importance value is determined by the total Gini reduction. 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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specifies a numeric variable that contains the weight of each observation....
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Parameter : Subparameter : Description
sample : rstore : specifies the options for sampling the shadow features
table : — : specifies the table name, caslib, and other common parameters....
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Parameter : Subparameter : Description
casOut : — : specifies the CAS table to store the analysis results.
saveState : — : specifies the CAS table to store the feature transformation and generation model....
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specifies the CAS table to store the analysis results. caslib : specifies the name of the caslib for the output table. indexVars : specifies the list of variables to create indexes for in the output data. lifetime : specifies the number of seconds to keep the table in memory after it is last accesse...
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specifies the names of variables to be copied to the output table....
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specifies the distinct count limit. If the limit is exceeded, and the misraGries parameter is set to True, the Misra-Gries frequency sketch algorithm is used to estimate the frequency distribution. Otherwise, the distinct count operation is aborted. Default: 10000 Minimum value: 256...
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specifies the tolerance value for the empirical cumulative distribution function. This value is used by the quantile sketch algorithm. Default: 0.001 Range: 1E-06–0.1...
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specifies the frequency variable....
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specifies that levels, instead of raw values, be generated. Default: FALSE...
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specifies the variables to use for the analysis. You can specify a subset of the variables from the input table. For more information about specifying the inputs parameter, see the common casinvardesc parameter (Appendix A: Common Parameters). Alias: vars...
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