The arboretum procedure
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- PROC FREQ Misclassification Table for the Training Data
- PROC GPLOT Plot of the Classification Results PROC GGONTOUR Plots of the Posterior Probabilities
- PROC GCONTOUR Plots of the Hidden Units
Notice that the MLP network with 3 hidden units correctly classifies all cases in the training data set. MLP with 3 Hidden Units Fits Statistics for the Training Data Set Train: Train: Train: Train: Root Akaike's Average Maximum Final Information Squared Absolute Prediction Criterion. Error. Error. Error. 34.0000 3.7039E-14 .0000030981 .00000020177
Train: Train: Number Misclassification of Wrong Rate. Classifications. 0 0
MLP with 3 Hidden Units TABLE OF F_C BY I_C F_C(From: C) I_C(Into: C) Frequency | Percent | Row Pct | Col Pct | 1| 2| 3| Total | | | | -------------+--------+--------+--------+ 1 | 8 | 0 | 0 | 8 | 4.44 | 0.00 | 0.00 | 4.44 | 100.00 | 0.00 | 0.00 | | 100.00 | 0.00 | 0.00 | -------------+--------+--------+--------+ 2 | 0 | 59 | 0 | 59 | 0.00 | 32.78 | 0.00 | 32.78 | 0.00 | 100.00 | 0.00 | | 0.00 | 100.00 | 0.00 | -------------+--------+--------+--------+ 3 | 0 | 0 | 113 | 113 | 0.00 | 0.00 | 62.78 | 62.78 | 0.00 | 0.00 | 100.00 | | 0.00 | 0.00 | 100.00 | -------------+--------+--------+--------+ Total 8 59 113 180 4.44 32.78 62.78 100.00 PROC GPLOT Plot of the Classification Results PROC GGONTOUR Plots of the Posterior Probabilities The legend at the bottom of the chart identifies the target level. Copyright 2000 by SAS Institute Inc., Cary, NC, USA. All rights reserved. PROC GPLOT creates a scatter plot of the Rings training data. proc gplot data=sampsio.dmdring; plot y*x=c /haxis=axis1 vaxis=axis2; symbol c=black i=none v=dot; symbol2 c=red i=none v=square; symbol3 c=green i=none v=triangle; axis1 c=black width=2.5 order=(0 to 30 by 5); axis2 c=black width=2.5 minor=none order=(0 to 20 by 2); title 'Plot of the Rings Training Data'; run;
The PROC NEURAL statement invokes the procedure. The DATA= option identifies the training data set that is used to fit the model. The DMDBCAT= option identifies the DMDB catalog. The RANDOM= option specifies the random number seed. proc neural data=sampsio.dmdring dmdbcat=sampsio.dmdring random=789;
The INPUT statement specifies an interval input layer. The LEVEL= option specifies the measurement level. The ID= option specifies an identifier for the interval input layer. input x y / level=interval id=i;
The TARGET statement defines an output layer. The output layer computes predicted values and compares those predicted values with the value of the target variable. The ID= option specifies an identifier for the output layer. The LEVEL= option specifies the target measurement level. By default, for nominal targets the combination function is set to linear, the activation function is set to mlogistic, and the error function is set to mbernoulli. target c / id=o level=nominal; The HIDDEN statement defines the number of hidden units that are used to perform the internal computations. By default, the input units are connected to each hidden unit and each hidden unit is connected to the output unit. The ID= option specifies an identifier for the hidden unit.
hidden 3 / id=h; The PRELIM statement causes the procedure to search for the best starting weights for subsequent training. The integer value of 5 specifies to use 5 preliminary runs. The weights from the seed with the smallest objective function among all runs is chosen. Preliminary training may help prevent the network from converging in a local minima. prelim 5;
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