The arboretum procedure
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- PRUNE Statement Deletes all nodes descended from any specified node. Interaction
- SCORE Statement Specifies that the data be scored. SCORE DATA=
- OUT= SAS-data-set
- Interaction
- NOLEAFID
- TARGET Statement Specifies an output variable. TARGET
- Details Missing Values
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. The SPLIT Procedure PRUNE Statement Deletes all nodes descended from any specified node. Interaction: The PRUNE statement requires an INTREE= or INDMSPLIT procedure option. PRUNE list-of-node-identifiers; Required Argument list-of-node-identifiers Specifies the nodes that will have no children. Range: Integer > 0 Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The SPLIT Procedure SCORE Statement Specifies that the data be scored. SCORE DATA= OUT=SAS-data-set <score-option(s)> NODES=node-list>; Required Arguments DATA= Specifies input data that contains inputs and, optionally, targets. OUT=SAS-data-set Output data set with outputs. Options DUMMY Includes dummy variables for each node. For each observation the value of the dummy variables is 1 if the observation appears in the node and 0 if it does not.
Specifies a list of nodes used to score the observations. If an observation does not fall into any node list, it does not contribute to the statistics and is not output. If an observation occurs in more than one node, it contributes multiple times to the statistics and is output once for each node it occurs in.
The NODES= option requires the INTREE= or INDMSPLIT procedure option.
The default is the list of leaf nodes. Omitting the NODES= option results in the decisions, utilities, and leaf assignment being output for each observation in the DATA= data set. NOLEAFID Does not include lead identifiers or node numbers. NOPRED Does not include predicted values. OUTFIT=SAS-data-set Output data set with fit statistics. ROLE=role-value Specifies the role of the DATA= data set. The ROLE= option primarily affects what fit statistics are computed and what their names and labels are. Role-value can be: TRAIN
The default when DATA= data set name in the PROC statement is the same as the data set name in the SCORE statement. VALID | VALIDATION The default when DATA= data set name in the SCORE statement is the same as DATA= data set name in the VALIDATA= option in the PROC statement. TEST
The default when DATA= data set name in the SCORE statement is not the same as the data set name in the DATA= or VALIDATA= option in the PROC statement. SCORE Residuals, computed profit, and fit statistics are not produced. Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. The SPLIT Procedure TARGET Statement Specifies an output variable. TARGET variable < / LEVEL=measurement>; Required Argument variable Specifies the variable that the model-fitting tries to predict. Options LEVEL=measurement Specifies the measurement level, where measurement can be: BINARY NOMINAL
ORDINAL INTERVAL
Default: LEVEL=INTERVAL. Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The SPLIT Procedure Details Missing Values Observations in which the target value is missing are ignored when training or validating the tree. If EXCLUDEMISS is specified, then observations with missing values are excluded during the search for a splitting rule. A search uses only one variable, and so only the observations missing on the single candidate input are excluded. An observation missing input x but not missing input y is used in the search for a split on y but not x. After a split is chosen, the rule is amended to assign missing values to the largest branch. If EXCLUDEMISS is not specified, the search for a split on an input treats missing values as a special, acceptable value, and includes them in the search. All observations with missing values are assigned to the same branch. The branch may or may not contain other observations. The branch chosen is the one that maximizes the split worth. For splits on a categorical variable, this amounts to treating a missing value as a separate category. For numerical variables, it amounts to treating missing values as having the same unknown non-missing value. One advantage of using missing data during the search is that the worth of split is computed with the same number of observations for each input. Another advantage is that an association of the missing values with the target values can contribute to the predictive ability of the split. One disadvantage is that missing values could unjustifiably dominate the choice of split. When a split is applied to an observation in which the required input value is missing, surrogate splitting rules are considered before assigning the observation to the branch for missing values. A surrogate splitting rule is a backup to the main splitting rule. For example, the main splitting rule might use county as input and the surrogate might use region. If the county is unknown and the region is known, the surrogate is used. If several surrogate rules exist, each surrogate is considered in sequence until one can be applied to the observation. If none can be applied, the main rule assigns the observation to the branch designated for missing values. The surrogates are considered in the order of their agreement with the main splitting rule. The agreement is measured as the proportion of training observations it and the main rule assign to the same branch. The measure excludes the observations that the main rule cannot be applied to. Among the remaining observations, those on which the surrogate rule cannot be applied count as observations not assigned to the same branch. Thus, an observation with a missing value on the input used in the surrogate rule but Yüklə 3,07 Mb. Dostları ilə paylaş:
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