PROC G3D creates a plot of the predicted values. Note that this network
underfits badly.
proc g3d data=mlpout;
plot x2*x1=p_hipl / grid side ctop=blue
caxis=green ctext=black
zmin=-1.5 zmax=1.5;
run;
The NEURAL Procedure
References
Berry, M. J. A. and Linoff, G. (1997), Data Mining Techniques for Marketing, Sales, and
Customer Support, New York: John Wiley and Sons, Inc.
Bishop, C. M. (1995), Neural Networks for Pattern Recognition, New York: Oxford University
Press.
Bigus, J. P. (1996), Data Mining with Neural Networks: Solving Business Problems - from
Application Development to Decision Support, New York: McGraw-Hill.
Collier Books (1987), The 1987 Baseball Encyclopedia Update, New York: Macmillan
Publishing Company.
Michie, D., Spiegelhalter, D. J. and Taylor, C. C. (1994), Machine Learning, Neural and
Statistical Classification, New York: Ellis Horwood.
Ripley, B. D. (1996), Pattern Recognition and Neural Networks, New York: Cambridge
University Press.
Sarle, W. S. (1994a), "Neural Networks and Statistical Models," Proceedings of the Nineteenth
Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc., 1538-1550.
Sarle, W. S. (1994b), "Neural Network Implementation in SAS Software," Proceedings of the
Nineteenth Annual SAS Users Group International Conference, Cary, NC: SAS Institute Inc.,
1550-1573.
Sarle, W. S. (1995), "Stopped Training and Other Remedies for Overfitting," Proceedings of the
27th Symposium on the Interface, Cary, NC: SAS Institute Inc.
Smith, M. (1993), Neural Networks for Statistical Modeling, New York: Van Nostrand
Reinhold.
Weiss, S. M. and Kulikowski, C. A., (1991), Computer Systems that Learn: Classification and
Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems, San
Mateo, CA: Morgan Kaufmann.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The PMBR Procedure
The PMBR Procedure
Overview
Procedure Syntax
PROC PMBR Statement
VAR Statement
TARGET Statement
CLASS Statement
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The PMBR Procedure
Overview
The PMBR procedure is used for prediction as an alternative to other predictive modeling techniques in
Enterprise Miner, such as the NEURAL, SPLIT, DMSPLIT, DMNEURL, and DMREG procedures.
However, the technique in the PMBR procedure is different. Whereas all the other techniques attempt to
determine some rules for predicting future examples, the PMBR procedure categorizes an observation in
a score data set by retrieving its k closest neighbors from a training data set, and then having each
neighbor vote on the target value based on its value for the target variable. These votes then become the
posterior pobabilities for predicting the target, which are included in an output data set. Training is thus
faster than with the alternative techniques, but scoring is generally slower.
The target variable is expected to be either binary, interval, or nominal. Ordinal targets are not specially
supported at this time, but could be modeled as interval targets. If the target variable is a class variable in
the DMDB, one variable is created on the output data set for each value of the target, representing the
appropriate posterior probabilities. Otherwise, one predicted variable is created on the output data set
corresponding to the average prediction for the k neighbors.
The neighbors are determined by a simple Euclidean distance between the values on each of the
variables in the VAR statement for the probe and target example. Thus, it is assumed that the variables
are orthogonal to each other and standardized. If your input data is not in that form, you need precede
this procedure with one that will create numeric, orthogonal, and standardized variables -- such as the
PRIMCOMP, DMNEURL, PRINQUAL, CORRESP, SPSVD procedures.
The PMBR procedure needs to be run separately and be given the DMDB-name for each of the data sets
to be scored, including any training, validation, test, or score data set.
Missing values in either the training or score data set are replaced by the mean of that variable as stored
in the DMDB catalog.
Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved.
The PMBR Procedure
PROC PMBR Statement
Invokes the PMBR procedure.
PROC PMBR < option(s)>;
Required Arguments
DMDBCAT = SAS-catalog
Specifies the DMDB catalog.
Options
DATA = (or IN =) SAS-data-set
Specifies the DMDB-encoded input SAS data to be trained on. If you omit the DATA= option, the
procedure uses the most recently created SAS data set, which must be DMDB-encoded.
SCORE = SAS-data-set
Specifies the data set to be scored. This data set might not have the target variable. It can be the
same name as the training data set.
OUT = SAS-data-set
Specifies the name of the output data set. This output data set contains all variable in the score
data set and additional variables representing the posterior probabilities. If the target variable is
categorical, the names of these variables generally begin with P_, followed by a part of the
original variable names and with the values added to the end. These posterior probabilities
correspond to the percentages of the k neighbors that have the value as the target. If the target
variable is interval, a single posterior variable is produced that averages the target values across
the k neighbors. This option is required if the SCORE= option is used.
K = integer
Specifies the number of nearest neighbors to retrieve.
Default:
1
PRINT
Prints out training information and weights (if the WEIGHTED option is specified) to the
OUTPUT window.
METHOD = method
Determines what data representation is used to store the training data set and then to retrieve the
nearest neighbors. The following methods are available:
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