Modeling and Multivariate Methods - SAS

Chapter 13 Recursively Partitioning Data 327
Partition Method
K Fold Crossvalidation shows a Crossvalidation report, giving fit statistics for both the training and
folded sets. For more information on validation, see “Validation” on page 335.
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.
Show Fit Details shows several measures of fit and a confusion matrix. The confusion matrix is a
two-way classification of actual and predicted response. This is for categorical responses only.
Entropy RSquare compares the log-likelihoods from the fitted model and the constant probability
model.
Generalized RSquare is a generalization of the Rsquare measure that simplifies to the regular Rsquare
for continuous normal responses. It is similar to the Entropy RSquare, but instead of using the
log-likelihood, it uses the 2/n root of the likelihood. It is scaled to have a maximum of 1. The value is 1
for a perfect model, and 0 for a model no better than a constant model.
Mean -Log p is the average of -log(p), where p is the fitted probability associated with the event that
occurred.
RMSE is the root mean square error, where the differences are between the response and p (the fitted
probability for the event that actually occurred).
Mean Abs Dev is the average of the absolute values of the differences between the response and p (the
fitted probability for the event that actually occurred).
Misclassification Rate is the rate for which the response category with the highest fitted probability is
not the observed category.
For Entropy RSquare and Generalized RSquare, values closer to 1 indicate a better fit. For Mean -Log p,
RMSE, Mean Abs Dev, and Misclassification Rate, smaller values indicate a better fit.
Save Columns
is a submenu for saving model and tree results, and creating SAS code.
Save Residuals saves the residual values from the model to the data table.
Save Predicteds saves the predicted values from the model to the data table.
Save Leaf Numbers saves the leaf numbers of the tree to a column in the data table.
Save Leaf Labels saves leaf labels of the tree to the data table. The labels document each branch that
the row would trace along the tree, with each branch separated by “&”. An example label could be
“size(Small,Medium)&size(Small)”. However, JMP does not include redundant information in the form
of category labels that are repeated. When a category label for a leaf references an inclusive list of
categories in a higher tree node, JMP places a caret (‘^”) where the tree node with redundant labels
occurs. Therefore, “size(Small,Medium)&size(Small)” is presented as ^&size(Small).
Save Prediction Formula saves the prediction formula to a column in the data table. The formula is
made up of nested conditional clauses that describe the tree structure. The column includes a Response
Probability property.
Save Tolerant Prediction Formula saves a formula that predicts even when there are missing values.
The column includes a Response Probability property.
Save Leaf Number Formula saves a column containing a formula in the data table that computes the
leaf number.