TREESPLIT Procedure

uses a data distribution and row order as determined by a previous partition action call. For more information, see the section The APPLYROWORDER Option in Chapter 2, Shared Concepts.

specifies how to handle missing values of predictor variables during training and how to handle missing values and unknown levels of predictor variables. An unknown level of a categorical predictor variable is a level that does not exist in the training data but is encountered during scoring. During scoring, unknown levels are treated as missing values.

You can specify one of the following values:

By default, ASSIGNMISSING=USEINSEARCH.

specifies how to bin interval input variables prior to growing the decision tree. The number of bins that are created is determined by the NUMBIN= option.

You can specify one of the following values:

By default, BINMETHOD=QUANTILE.

determines the splits at each node using clustering on each input variable, and then chooses the splitting variable on the basis of which variable and split optimize the criterion that is specified in the GROW statement.

requests a table of the results of cost-complexity pruning based on cross validation. For each penalty parameter in the cross validation, the table provides the penalty parameter, the minimum, the maximum, and the average error. The error is the misclassification rate when the response variable is categorical and is the average square error (ASE) when the response variable is continuous. You can use the PLOTS=CVCC option to request a plot of the information in this table.

names the input data table for PROC TREESPLIT to use. libref.data-table is a two-level name, where

specifies the data table that you have previously saved as a tree model by using the OUTMODEL= option in a previous run of PROC TREESPLIT. libref.data-table is a two-level name, where libref refers to the library, and data-table specifies the name of the input data table. For more information about this two-level name, see the DATA= option and the section Using CAS Sessions and CAS Engine Librefs.

When you use the INMODEL= option, the OUTPUT statement is required and any other options in the PROC TREESPLIT statement, except for NOPRINT and PLOTS, are ignored.

The data table used in the INMODEL= option must include the attributes that are associated with the table produced by the OUTMODEL= option, or PROC TREESPLIT issues an error.

specifies the maximum number of child nodes per parent node in the tree. PROC TREESPLIT tries to create this number of children unless it is impossible (for example, if a split variable does not have enough levels).

By default, MAXBRANCH=2.

specifies the maximum depth of the tree to be grown. The number of levels in a tree is equal to the depth plus 1. The default is calculated by the following equation, where b is the value of the MAXBRANCH= option in the PROC TREESPLIT statement:

This value can be tuned with the AUTOTUNE statement. In the ODS output that corresponds to tables that are generated using the AUTOTUNE statement, the term "MAXLEVEL" is used. The value of MAXLEVEL is equal to the value of the MAXDEPTH= option plus 1.

specifies the minimum number of observations in the training data that each child of a split must contain in order for the split to be considered.

By default, MINLEAFSIZE=5. This value can be tuned with the AUTOTUNE statement.

specifies a threshold for using missing values in the split search when ASSIGNMISSING=USEINSEARCH as the missing value policy. If the number of observations that have missing values for the splitting variable is greater than or equal to number, then PROC TREESPLIT uses the USEINSEARCH policy for missing values.

By default, MINUSERINSEARCH=1.

specifies search methods for splitting on a nominal variable. For more information, see the section Splitting Nominal Values.

If you do not specify this option, the default values of the suboptions determine the search method. You can specify one or more of the following suboptions:

suppresses the generation of ODS output.

specifies the number of threads to use in the computation. The default value is the number of CPUs available on the machine.

specifies the number of bins to use for binning interval predictor variables. PROC TREESPLIT bins continuous predictors to a fixed bin size. This option controls the number of bins and thereby also the size of the bins.

By default, NUMBIN=50. This value can be tuned with the AUTOTUNE statement.

specifies the data table to which to save the decision tree model. libref.data-table is a two-level name, where libref refers to the library, and data-table specifies the name of the output data table. For more information about this two-level name, see the DATA= option and the section Using CAS Sessions and CAS Engine Librefs.

The data table that results from this option contains information about each node in the decision tree model, including the splitting variables, the child nodes, the number of observations at each node, and the predicted response at each node. This table also has attributes attached that contain information about the input variables, the target variable, and the decision tree model.

controls the plots that are produced through ODS Graphics. When you specify only one plot-request, you can omit the parentheses around it.

You can specify the following global-plot-option:

You can specify the following plot-requests:

outputs tables that indicate generated columns in the OUT= table from the OUTPUT statement. For a continuous response, PROC TREESPLIT generates an output table named PredName, which indicates the name of the predicted value column. For a categorical response, PROC TREESPLIT generates an output table named PredIntoName, which indicates the name of the predicted value column, and also an output table named PredProbName, which indicates the names of the predicted probability columns.

outputs a table of the pruning results.

creates a variable importance table by using random branch assignment (RBA). This table is created in addition to the normal variable importance table that is calculated using the residual sum of squares (RSS) error. For more information about RBA and RSS variable importance, see the section Variable Importance.

specifies the initial seed for random number generation. The seed is used in the AUTOTUNE statement, in the CROSSVALIDATION statement, and for k-fold cross validation pruning. The value of number must be an integer. By default, the seed is generated by reading the time of day from the computer’s clock.

splits predictor variables only once on a branch. When you specify this option, a variable cannot be split more than once on the path from the root to any particular terminal node. However, a variable can be used more than once across branches.

creates a table that contains the information used to create the WholeTree and ZoomedTree ODS graphics.

calculates the variable interaction importance, which is described in the section Variable Interaction Importance. n can be one of the following values:

If you do not specify this option, then only the importance of individual variables without interactions is calculated.