Modeling and Multivariate Methods - SAS

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288 Creating Neural Networks Chapter 10 Model Options Table 10.8 Model Report Options (Continued) Categorical Profiler Contour Profiler Surface Profiler ROC Curve Lift Curve Plot Actual by Predicted Plot Residual by Predicted Save Formulas Save Profile Formulas Save Fast Formulas Make SAS Data Step Launches the Prediction Profiler. Similar to the Profiler option, except that all categorical probabilities are combined into a single profiler row. Available only for nominal or ordinal responses. For details about the options in the red triangle menu, see the “Visualizing, Optimizing, and Simulating Response Surfaces” chapter on page 553. Launches the Contour Profiler. This is available only when the model contains more than one continuous factor. For details about the options in the red triangle menu, see the “Visualizing, Optimizing, and Simulating Response Surfaces” chapter on page 553. Launches the Surface Profiler. This is available only when the model contains more than one continuous factor. For details about the options in the red triangle menu, see the “Visualizing, Optimizing, and Simulating Response Surfaces” chapter on page 553. Creates an ROC curve. Available only for nominal or ordinal responses. For details about ROC Curves, see “ROC Curve” on page 337 in the “Recursively Partitioning Data” chapter. Creates a lift curve. Available only for nominal or ordinal responses. For details about Lift Curves, see “Lift Curves” on page 339 in the “Recursively Partitioning Data” chapter. Plots the actual versus the predicted response. Available only for continuous responses. Plots the residuals versus the predicted responses. Available only for continuous responses. Creates new columns in the data table containing formulas for the predicted response and the hidden layer nodes. Creates new columns in the data table containing formulas for the predicted response. Formulas for the hidden layer nodes are embedded in this formula. This option produces formulas that can be used by the Flash version of the Profiler. Creates new columns in the data table containing formulas for the predicted response. Formulas for the hidden layer nodes are embedded in this formula. This option produces formulas that evaluate faster than the other options, but cannot be used in the Flash version of the Profiler. Creates SAS code that you can use to score a new data set.
Chapter 10 Creating Neural Networks 289 Example of a Neural Network Table 10.8 Model Report Options (Continued) Save Validation Save Transformed Covariates Remove Fit Creates a new column in the data table that identifies which rows were used in the training and validation sets. This option is not available when a Validation column is specified on the Neural launch window. See “The Neural Launch Window” on page 280. Creates new columns in the data table showing the transformed covariates. The columns contain formulas that show the transformations. This option is available only when the Transform Covariates option is checked on the Model Launch. See “Fitting Options” on page 285. Removes the entire model report. Example of a Neural Network This example uses the Boston Housing.jmp data table. Suppose you want to create a model to predict the median home value as a function of several demographic characteristics. Follow the steps below to build the neural network model: 1. Launch the Neural platform by selecting Analyze > Modeling > Neural. 2. Assign mvalue to the Y, Response role. 3. Assign the other columns (crim through lstat) to the X, Factor role. 4. Click OK. 5. Enter 0.2 for the Holdback Proportion. 6. Enter 3 for the number of TanH nodes in the first layer. 7. Check the Transform Covariates option. 8. Click Go. The report is shown in Figure 10.6. Note: Results will vary due to the random nature of choosing a validation set. Figure 10.6 Neural Report
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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12 Examining the Residuals . . . .
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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Contents Launch the Fit Model Platf
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158 Fitting Multiple Response Model
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338 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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Contents The Categorical Platform .
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386 Performing Categorical Response
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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654 Statistical Details Appendix A
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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