Launch the Boosted Tree Platform

Launch the Boosted Tree platform by selecting Analyze > Predictive Modeling > Boosted Tree.

Figure 6.6 Boosted Tree Launch Window Using Body Fat.jmp 

For more information about the options in the Select Columns red triangle menu, see “Column Filter Menu” in Using JMP.

The Boosted Tree platform launch window has the following options:

Y, Response

The response variable or variables that you want to analyze.

X, Factor

The predictor variables.

Weight

A column whose numeric values assign a weight to each row in the analysis.

Freq

A column whose numeric values assign a frequency to each row in the analysis.

Validation

A numeric column that defines the validation sets. This column should contain at most three distinct values:

– If the validation column has two levels, the smaller value defines the training set and the larger value defines the validation set.

– If the validation column has three levels, the values, in order of increasing size, define the training, validation, and test sets.

– If the validation column has more than three levels, the rows that contain the smallest three values define the validation sets. All other rows are excluded from the analysis.

The Boosted Tree platform uses the validation column to train and tune the model or to train, tune, and evaluate the model. For more information about validation, see “Validation in JMP Modeling”.

If you click the Validation button with no columns selected in the Select Columns list, you can add a validation column to your data table. For more information about the Make Validation Column utility, see “Make Validation Column”.

By

A column or columns whose levels define separate analyses. For each level of the specified column, the corresponding rows are analyzed using the other variables that you have specified. The results are presented in separate reports. If more than one By variable is assigned, a separate report is produced for each possible combination of the levels of the By variables.

Method

Enables you to select the partition method (Decision Tree, Bootstrap Forest, Boosted Tree, K Nearest Neighbors, or Naive Bayes). These alternative methods, except for Decision Tree, are available in JMP Pro.

For more information about these methods, see “Partition Models”, “Bootstrap Forest”, “K Nearest Neighbors”, and “Naive Bayes”.

Validation Portion

The portion of the data to be used as the validation set.

Informative Missing

If selected, enables missing value categorization for categorical predictors and informative treatment of missing values for continuous predictors. See “ROC Curve”.

Ordinal Restricts Order

If selected, restricts consideration of splits to those that preserve the ordering.