Modeling and Multivariate Methods - SAS

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318 Recursively Partitioning Data Chapter 13 Partition Method Table 13.1 Descriptions of Launch Window Option Description Y, Response Choose the response variable. X, Factor Choose the predictor variables. Validation Missing Value Categories Method Validation Portion Enter a validation column here. For more information on validation, see “Validation” on page 335. Check this box to enable missing value categorization for nominal or ordinal predictors and responses. This option does not impact continuous predictors or responses. For more details on this option, or for complete details on how the Partition platform handles missing values, see “Missing Values” on page 340. Select the partition method: • Decision Tree • Bootstrap Forest • Boosted Tree For more information on the methods, see “Partition Method” on page 318. The Bootstrap Forest and Boosted Tree methods are available only in JMP Pro. Enter the portion of the data to be used as the validation set. For more information on validation, see “Validation” on page 335. Partition Method The Partition platform provides three methods for producing a final tree: • For the Decision Tree method, see “Decision Tree” on page 318. • For the Bootstrap Forest method, see “Bootstrap Forest” on page 329. • For the Boosted Tree method, see “Boosted Tree” on page 332. Decision Tree The Decision Tree method makes a single pass through the data and produces a single tree. You can interactively grow the tree one split at a time, or grow the tree automatically if validation is used.
Chapter 13 Recursively Partitioning Data 319 Partition Method Decision Tree Report As an example, use the Boston Housing.jmp data table. Launch the Partition platform and assign mvalue to the Y, Response role. Assign all the other variables to the X, Factor role. If using JMP Pro, select Decision Tree from the Method menu, then click OK. The initial report is shown in Figure 13.3. Figure 13.3 Decision Tree Initial Report The Split button is used to partition the data, creating a tree of partitions. Repeatedly splitting the data results in branches and leaves of the tree. This can be thought of as growing the tree. The Prune button is used to combine the most recent split back into one group. Note the reported statistics: RSquare is the current R 2 value. N is the number of observations (if no Freq variable is used). Number of Splits is the current number of splits. AICc Count Mean is the corrected Akaike’s Information Criterion. gives the number of rows in the branch. gives the average response for all rows in that branch. Std Dev gives the standard deviation of the response for all rows in that branch.
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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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Contents Launch the Fit Model Platf
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Contents Example of a Multiple Resp
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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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368 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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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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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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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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