Bootstrap Forest Platform Options
The Bootstrap Forest red triangle menu contains the following options:
Plot Actual by Predicted
(Available only for continuous responses.) Shows or hides a plot of actual versus predicted values.
Column Contributions
Shows or hides a report of each input column’s contribution to the fit. The report also contains the following information:
– The total number of instances over all of the trees when the specified column is used to split the data.
– The total G2 (for a categorical response) or SS, sum of squares (for a continuous response), attributed to the column.
– A bar chart of G2 or SS.
– The proportion of G2 or SS attributed to the column.
Show Trees
Provides various options for displaying trees in the Tree Views report. The report gives a picture of the tree that is fit at each layer of the boosting process. For a description of the Prob column shown by the Show names categories estimates option, see “Predicted Probabilities in Tree Methods”.
ROC Curve
(Available only for categorical responses.) Shows or hides the Receiver Operating Characteristic (ROC) plot that contains a curve for each level of the response variable. If you used validation, a plot is shown for each of the training, validation, and test sets. See “ROC Curve”.
Precision Recall Curve
(Available only for categorical responses.) Shows or hides the Precision-Recall Curve plot that contains a curve for each level of the response variable. A precision-recall curve plots the precision values against the recall values at a variety of thresholds. If you used validation, a plot is shown for each of the training, validation, and test sets. See “Precision-Recall Curve”.
Lift Curve
(Available only for categorical responses.) Shows or hides the Lift Curve plot. If you used validation, a plot is shown for each of the training, validation, and test sets. See “Lift Curve”.
Decision Threshold
(Available only for binary categorical responses.) Shows or hides Decision Thresholds reports for the training, validation, and test sets, if specified. Each report contains a graph of the distribution of fitted probabilities for each model, confusion matrices for each model, classification graphs to compare the model fits, and a table of classification accuracy metrics. See “Decision Thresholds Report”.
Save Columns
Contains options 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. The formula consists of nested conditional clauses that describe the tree structure. If the response is continuous, the column contains a Predicting property. If the response is categorical, the column contains a Response Probability property.
Save Tolerant Prediction Formula
(The Save Prediction Formula option should be used instead of this option. Use this option only when Save Prediction Formula is not available.) Saves a formula that predicts even when there are missing values and when Informative Missing has not been selected. The prediction formula tolerates missing values by randomly allocating response values for missing predictors to a split. If the response is continuous, the column contains a Predicting property. If the response is categorical, the column contains a Response Probability property. If you have selected Informative Missing, you can save the Tolerant Prediction Formula by holding the Shift key as you click the report’s red triangle.
Save Residuals
(Available only for continuous responses.) Saves the residuals to the data table.
Save Cumulative Details
(Available only if validation is used.) Creates a data table containing the fit statistics for each tree.
Publish Prediction Formula
Creates a prediction formula and saves it as a formula column script in the Formula Depot platform. If a Formula Depot report is not open, this option creates a Formula Depot report. See “Formula Depot”.
Publish Tolerant Prediction Formula
(The Publish Prediction Formula option should be used instead of this option. Use this option only when Publish Prediction Formula is not available.) Creates a tolerant prediction formula and saves it as a formula column script in the Formula Depot platform. If a Formula Depot report is not open, this option creates a Formula Depot report. See “Formula Depot”. If you have selected Informative Missing, you can use this option by holding the Shift key as you click the report’s red triangle.
Make SAS DATA Step
Creates SAS code for scoring a new data set.
Specify Profit Matrix
(Available only for categorical responses.) Enables you to specify profit or costs associated with correct or incorrect classification decisions. See “Show Fit Details”.
Profiler
Shows or hides a Prediction Profiler. See “Profiler” in Profilers.
See “Local Data Filters in JMP Reports”, “Redo Menus in JMP Reports”, “Group Platform”, and “Save Script Menus in JMP Reports” in Using JMP for more information about the following options:
Local Data Filter
Shows or hides the local data filter that enables you to filter the data used in a specific report.
Redo
Contains options that enable you to repeat or relaunch the analysis. In platforms that support the feature, the Automatic Recalc option immediately reflects the changes that you make to the data table in the corresponding report window.
Platform Preferences
Contains options that enable you to view the current platform preferences or update the platform preferences to match the settings in the current JMP report.
Save Script
Contains options that enable you to save a script that reproduces the report to several destinations.
Save By-Group Script
Contains options that enable you to save a script that reproduces the platform report for all levels of a By variable to several destinations. Available only when a By variable is specified in the launch window.
Note: Additional options for this platform are available through scripting. Open the Scripting Index under the Help menu. In the Scripting Index, you can also find examples for scripting the options that are described in this section.