output data sets. You also need the following statements:
A DECISION statement
Specifies the input data set that contains the output from a modeling procedure.
Specifies the output data set that contains the following variables:
the variables from the input data set
the consequence of the chosen decision computed from the target value (prefix CL_ or CP_)
the consequence of the best possible decision knowing the target value (prefix BL_ or BP_)
If PRIORVAR= and OLDPRIORVAR= variables are specified, then the output data will contain
the recalculated posteriors.
Note: If you want to create a permanent SAS data set, you must specify a two-level name. For
more information on this topic, see "SAS Files" and "DATA Step Concepts " in SAS Language
If the OUT= option is omitted, PROC DECIDE creates an output data set and
names it according to the DATAn convention, just as if you had omitted a
data set name in a DATA statement.
Specifies the output data set that contains statistics including the total and average profit or loss.
The OUTFIT= option may not be specified with ROLE=SCORE.
Specifies whether the DATA= data set is a training set, validation set, test set, or scoring set. The
ROLE= option affects the names of the variables in the OUTFIT= data set.
You may specify both FILE= and METABASE= to write code to both locations. The TARGET
variable must appear in the DATA= data set as well as the DECDATA= data set.
Specifies a path for writing the code to an external file. For example:
Specifies the numeric format to be used when printing numeric constants. For example,
Specifies the group identifier (up to four characters) for group processing.
Specifies a catalog entry to which the code is written. For example, METABASE=
Specifies that variables that depend on the target variable, such as the BL_, BP_, CL_, and CP_
variables, are to be computed in the code.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
NEURAL, or SPLIT procedures.
Specifies the input data set that contains the decision matrix and/or prior probabilities. The
DECDATA= data set must also contain the target variable.
The DECDATA= data set may contain decision variables specified by means of the DECVARS=
option, or prior probability variable(s) specified by means of the PRIORVAR= option and/or the
OLDPRIORVAR= option, or both.
The target variable is specified by means of the TARGET statement in the DECIDE, NEURAL,
and SPLIT procedures or by using the MODEL statement in the DMREG procedure.
For a categorical target variable, there should be one row for each class. The value in the decision
matrix located at a given row and column specifies the consequence of making the decision
corresponding to column when the target class corresponds to the row. If any class appears twice
or more in the DECDATA= data set, an error message is printed and the procedure terminates. For
the DMREG, NEURAL, and SPLIT procedures, all class values in the training set must also
appear in the DECDATA= data set, but it is allowed to have class values in the DECDATA= data
set that are not in the training set. For the DECIDE procedure, any class value in the DATA= data
set that is not found in the DECDATA= data set is treated in the same way as a missing class
value; it is allowed to have class values in the DECDATA= data set that are not in the DATA=
data set, but note that the classes in the DECDATA= data set must correspond exactly with the
variables in the POSTERIORS statement.
For an interval target variable, each row defines a knot in a piecewise linear spline function. The
consequence of making a decision is computed by interpolating in the corresponding column of
the decision matrix. If the predicted target value is outside the range of knots in the decision
matrix, the consequence of a decision is computed by linear extrapolation. If the target values are
in nondecreasing or nonincreasing order, any interior target value is allowed to appear twice in the
data set so you can specify discontinuities. The end points (that is, the minimum and maximum
target values in the data set) may not appear more than once. No target value is allowed to appear
more than twice. If the target values are not in nondecreasing or nonincreasing order, the target