Partition Platform Options
The options in the Partition red triangle menu give you the ability to customize reports according to your needs. The available options are determined by the type of data that you use for your analysis.
Display Options
Contains options that show or hide report elements.
Show Points
Shows the points. For categorical responses, this option shows the points or colored panels.
Show Tree
Shows the large tree of partitions.
Show Graph
Shows the partition graph.
Show Split Bar
(Available only for categorical responses.) Shows the colored bars that indicate the split proportions in each leaf.
Show Split Stats
Shows the split statistics. For more information about the categorical split statistic G2, see Statistical Details for the Partition Platform.
Show Split Prob
(Available only for categorical responses.) Shows the Rate and Prob statistics in the node reports.
JMP automatically shows the Rate and Prob statistics when you select Show Split Count. For more information about Rate and Prob, see Statistical Details for the Partition Platform.
Show Split Count
(Available only for categorical responses.) Shows frequency counts in the node reports. When you select this option, JMP automatically selects Show Split Prob. And when you deselect Show Split Prob, the counts do not appear.
Show Split Candidates
Shows the Candidates report.
Sort Split Candidates
Sorts the Candidates reports by the statistic or the log(worth), whichever is appropriate.
Split Best
Splits the tree at the optimal split point. This is equivalent to clicking the Split button.
Prune Worst
Removes the terminal split that has the least discrimination ability. This is equivalent to clicking the Prune button.
Minimum Size Split
Define the minimum size split allowed by entering a number or a fractional portion of the total sample size. To specify a number, enter a value greater than or equal to 1. To specify a fraction of the sample size, enter a value less than 1. The default value is set to the maximum of 5, or the floor of the number of rows divided by 10,000.
Lock Columns
Interactively lock columns so that they are not considered for splitting. You can turn the display off or back on without affecting the individual locks.
Plot Actual by Predicted
(Available only for continuous responses.) Shows a plot of actual response values by predicted response values. When you fit a Decision Tree, all observations in a leaf have the same predicted value. If there are n leaves, then the Actual by Predicted plot shows at most n distinct predicted values. The actual values form a scatter of points around each leaf mean on n vertical lines.
The plot contains a diagonal line that is the Y = X line. For a perfect fit, all the points would be on this diagonal. When validation is used, plots are shown for both the training and the validation sets.
Small Tree View
Shows a small version of the partition tree to the right of the partition plot.
Tree 3D
Shows a 3-D plot of the tree structure. To access this option, press Shift and click the red triangle menu.
Leaf Report
Shows the mean and count or rates for the bottom-level leaves of the report.
Column Contributions
Shows a report indicating each input column’s contribution to the fit. The report also shows how many times it defined a split and the total G2 or Sum of Squares attributed to that column.
Split History
Shows a plot of RSquare versus the number of splits. If you use excluded row validation, holdback validation, or a validation column, separate curves are drawn for training and validation RSquare values. The RSquare curve is blue for the training set and red for the validation set. If you select K Fold Crossvalidation, the RSquare curve for all of the data is blue, and the curve for the cross validation RSquare is green.
ROC Curve
(Available only for categorical responses.) Shows or hides a Receiver Operating Characteristic (ROC) plot that contains a curve for each level of the response variable. ROC curves display the efficiency of a model’s fitted probabilities to sort the response levels. If you used validation, a plot is shown for each of the training, validation, and test sets. See ROC Curve.
Lift Curve
(Available only for categorical responses.) Shows or hides the Lift Curve plot. Lift curves display the predictive ability of a partition model. If you used validation, a plot is shown for each of the training, validation, and test sets. See Lift 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.
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.
Show Fit Details
(Appears only for categorical responses.) The Fit Details report shows several measures of fit and provides a Confusion Matrix report. See Show Fit Details.
Save Columns
Contains options 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. Each branch is separated by “&”. An example label might be: “size(Small,Medium)&size(Small)”. However, JMP does not include redundant information in the form of category labels that are repeated. A category label for a leaf might refer to an inclusive list of categories in a higher tree node. A caret (‘^”) appears where the tree node with redundant labels occurs. Therefore, “size(Small,Medium)&size(Small)” is presented as ^&size(Small).
Save Prediction Formula
Saves prediction formulas to a column or multiple columns in the data table. The formulas consist of nested conditional clauses that describe the tree structure. If the response is continuous, one column that contains a Predicting property is added. If the response is categorical, columns that contain a Response Probability property are added for each level of the response. In addition, a Most Likely column that contains the response level with the highest probability of occurrence for each observation is added.
Save Tolerant Prediction Formula
Saves a formula that predicts even when there are missing values and when Informative Missing has not been checked. 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 checked Informative Missing, you can save the Tolerant Prediction Formula by holding the Shift key as you click the report’s red triangle.
Save Leaf Number Formula
Saves a column containing a formula in the data table that computes the leaf number.
Save Leaf Label Formula
Saves a column containing a formula in the data table that computes the leaf label.
Make SAS DATA Step
Creates SAS code for scoring a new data set.
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
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 checked Informative Missing, you can use this option by holding the Shift key as you click the report’s red triangle.
Specify Profit Matrix
(Available only for categorical responses.) Enables you to specify profits or costs associated with correct or incorrect classification decisions. For a nominal response, you can specify the profit matrix entries using a probability threshold. See Specify Profit Matrix.
Profiler
Shows an interactive profiler report. Changes in the factor values are reflected in the estimated classification probabilities. See Profiler in Profilers.
Color Points
(Available only for categorical responses.) Colors points based on their response level. This is equivalent to clicking the Color Points button. See The Partition Report.
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.