Modeling and Multivariate Methods - SAS

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330 Recursively Partitioning Data Chapter 13 Partition Method Early Stopping is checked to perform early stopping. If checked, the process stops growing additional trees if adding more trees doesn’t improve the validation statistic. If not checked, the process continues until the specified number of trees is reached. This option appears only if validation is used. Multiple Fits over number of terms is checked to create a bootstrap forest for several values of Number of terms sampled per split. The lower value is specified above by the Number of terms samples per split option. The upper value is specified by the following option: Max Number of terms is the maximum number of terms to consider for a split. Bootstrap Forest Report The Bootstrap Forest report is shown in Figure 13.11. Figure 13.11 Bootstrap Forest The results on the report are described here: Model Validation - Set Summaries provides fit statistics for all the models fit if you selected the Multiple Fits option on the options window. Specifications provides information on the partitioning process. Overall Statistics provides fit statistics for both the training and validation sets.
Chapter 13 Recursively Partitioning Data 331 Partition Method Confusion Matrix provides two-way classifications of actual and predicted response levels for both the training and validation sets. This is available only with categorical responses. Cumulative Validation provides a plot of the fit statistics versus the number of trees. The Cumulative Details report below the plot is a tabulation of the data on the plot. This is only available when validation is used. Per-Tree Summaries gives summary statistics for each tree. Bootstrap Forest Platform Options The Bootstrap Forest report red-triangle menu has the following options: Show Trees is a submenu for displaying the Tree Views report. The report produces a picture of each component tree. None does not display the Tree Views Report. Show names displays the trees labeled with the splitting columns. Show names categories displays the trees labeled with the splitting columns and splitting values. Show names categories estimates displays the trees labeled with the splitting columns, splitting values, and summary statistics for each node. Plot Actual by Predicted responses. provides a plot of actual versus predicted values. This is only for continuous Column Contributions brings up a report showing how each input column contributed to the fit, including how many times it was split and the total G 2 or Sum of Squares attributed to that column. ROC Curve Lift Curve is described in the section “ROC Curve” on page 337. This is for categorical responses only. is described in the section “Lift Curves” on page 339. This is for categorical responses only. Save Columns is a submenu for saving model and tree results, and creating SAS code. Save Predicteds saves the predicted values from the model to the data table. Save Prediction Formula saves the prediction formula to a column in the data table. Save Tolerant Prediction Formula saves a formula that predicts even when there are missing values. Save Residuals saves the residuals to the data table. This is for continuous responses only. Save Cumulative Details creates a data table containing the fit statistics for each tree. Make SAS DATA Step creates SAS code for scoring a new data set. Make Tolerant SAS DATA Step creates SAS code that can score a data set with missing values. Script contains options that are available to all platforms. See Using JMP.
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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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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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380 Performing Time Series Analysis
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382 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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Contents Introduction to Item Respo
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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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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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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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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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