Modeling and Multivariate Methods - SAS

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280 Creating Neural Networks Chapter 10 Overview of Neural Networks The Neural Launch Window Use the Neural launch window to specify X and Y variables, a validation column, and to enable missing value coding. Figure 10.3 The Neural Launch Window Table 10.1 Description of the Neural Launch Window Y, Response Choose the response variable. When multiple responses are specified, the models for the responses share all parameters in the hidden layers (those parameters not connected to the responses). X, Factor Choose the input variables. Freq Validation By Missing Value Coding Choose a frequency variable. Choose a validation column. For more information, see “Validation Method” on page 282. Choose a variable to create separate models for each level of the variable. Check this box to enable missing value imputation and coding. If this option is not checked, rows with missing values are ignored. For continuous variables, missing values are replaced by the mean of the variable. Also, a missing value indicator variable is created and included in the model. If a variable is transformed, the imputation occurs after the transformation. For categorical variables, the missing values are not imputed, but are treated as another level of the variable in the model.
Chapter 10 Creating Neural Networks 281 Overview of Neural Networks The Model Launch Use the Model Launch dialog to specify the validation method, the structure of the hidden layer, whether to use gradient boosting, and other fitting options. Figure 10.4 The Model Launch Dialog JMP Pro JMP Table 10.2 Description of the Model Launch Dialog Validation Method Hidden Layer Structure or Hidden Nodes Boosting Fitting Options Select the method that you want to use for model validation. For details, see “Validation Method” on page 282. Note: The standard edition of JMP uses only the TanH activation function, and can fit only neural networks with one hidden layer. Specify the number of hidden nodes of each type in each layer. For details, see “Hidden Layer Structure” on page 283. Specify options for gradient boosting. For details, see “Boosting” on page 284. Specify options for variable transformation and model fitting. For details, see “Fitting Options” on page 285.
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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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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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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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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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100 Fitting Standard Least Squares
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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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Contents Introduction to Logistic M
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200 Performing Logistic Regression
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330 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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Contents The Categorical Platform .
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386 Performing Categorical Response
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Contents Introduction to Choice Mod
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404 Performing Choice Modeling Chap
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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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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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470 Clustering Data Chapter 18 K-Me
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472 Clustering Data Chapter 18 K-Me
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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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478 Clustering Data Chapter 18 Self
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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Overview of the Partial Le
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Contents Introduction to Item Respo
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526 Scoring Tests Using Item Respon
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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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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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550 Plotting Surfaces Chapter 23 Pl
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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644 References Hooper, J. H. and Am
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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Contents The Response Models . . .
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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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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