Modeling and Multivariate Methods - SAS

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334 Recursively Partitioning Data Chapter 13 Partition Method Confusion Matrix provides confusion statistics 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 stages. The Cumulative Details report below the plot is a tabulation of the data on the plot. This is only available when validation is used. Boosted Tree Platform Options The Boosted Tree 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 the tree at each stage of the boosting process. 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 the prediction formula to a column in the data. This formula can predict even with missing values. Save Residuals saves the residuals to the data table. This is for continuous responses only. Save Offset Estimates saves the offsets from the linear logits. This is for categorical responses only. Save Tree Details creates a data table containing split details and estimates for each stage. Save Cumulative Details creates a data table containing the fit statistics for each stage. 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.
Chapter 13 Recursively Partitioning Data 335 Validation Validation If you grow a tree with enough splits, partitioning can overfit data. When this happens, the model predicts the fitted data very well, but predicts future observations poorly. Validation is the process of using part of a data set to estimate model parameters, and using the other part to assess the predictive ability of the model. • The training set is the part that estimates model parameters. • The validation set is the part that assesses or validates the predictive ability of the model. • The test set is a final, independent assessment of the model’s predictive ability. The test set is available only when using a validation column (see Table 13.1). The training, validation, and test sets are created by subsetting the original data into parts. Table 13.2 describes several methods for subsetting a data set. Table 13.2 Validation Methods Excluded Rows Holdback KFold Uses row states to subset the data. Rows that are unexcluded are used as the training set, and excluded rows are used as the validation set. For more information about using row states and how to exclude rows, see Using JMP. Randomly divides the original data into the training and validation data sets. The Validation Portion (see Table 13.1) on the platform launch window is used to specify the proportion of the original data to use as the validation data set (holdback). Divides the original data into K subsets. In turn, each of the K sets is used to validate the model fit on the rest of the data, fitting a total of K models. The model giving the best validation statistic is chosen as the final model. KFold validation can be used only with the Decision Tree method. To use KFold, select K Fold Crossvalidation from the platform red-triangle menu, see “Platform Options” on page 325. This method is best for small data sets, because is makes efficient use of limited amounts of data.
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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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Contents Book Conventions. . . . .
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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386 Performing Categorical Response
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Contents Introduction to Choice Mod
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Contents Principal Components. . .
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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640 References Becker, R.A. and Cle
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650 References Umetrics (1995), Mul
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654 Statistical Details Appendix A
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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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