Launch the Bootstrap Forest Platform

Launch the Bootstrap Forest platform by selecting Analyze > Predictive Modeling > Bootstrap Forest.

Launch Window

Figure 5.7 Bootstrap Forest Launch Window

The Bootstrap Forest platform launch provides 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 contains at most three distinct values. See Validation in Partition Models.

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 details on these methods, see Partition Models, Boosted Tree, K Nearest Neighbors, and Naive Bayes.

Validation Portion

The portion of the data to be used as the validation set. See Validation in Partition Models.

Informative Missing

If selected, enables missing value categorization for categorical predictors and informative treatment of missing values for continuous predictors. See Informative Missing in Partition Models.

Ordinal Restricts Order

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

Specification Window

After you select OK in the launch window, the Bootstrap Forest Specification window appears.

Figure 5.8 Bootstrap Forest Specification Window

Specification Panel

Number of Rows

The number of rows in the data table.

Number of Terms

The number of columns that are specified as predictors.

Forest Panel

Number of Trees in the Forest

Number of trees to grow and then average.

Number of Terms Sampled per Split

Number of predictors to consider as splitting candidates at each split. For each split, a new random sample of predictors is taken as the candidate set.

Bootstrap Sample Rate

Proportion of observations to sample (with replacement) for growing each tree. A new random sample is generated for each tree.

Minimum Splits Per Tree

Minimum number of splits for each tree.

Maximum Splits Per Tree

Maximum number of splits for each tree.

Minimum Size Split

Minimum number of observations needed on a candidate split.

Early Stopping

(Available only if validation is used.) If selected, the process stops growing additional trees if adding more trees does not improve the validation statistic. The validation statistic is the validation set’s Entropy RSquare value for a categorical response and its RSquare value for a continuous response. If not selected, the process continues until the specified number of trees is reached.

Multiple Fits Panel

Multiple Fits over Number of Terms

If selected, creates a bootstrap forest for several values of number of terms sampled per split. The model for which results are displayed is the model whose Validation Set’s Entropy RSquare value (for a categorical response) or RSquare (for a continuous response) is the largest.

The lower bound is the Number of Terms Sampled per Split specification. The upper bound is specified by the following option:

Max Number of Terms

The maximum number of terms to consider for a split.

Use Tuning Table Design

Opens a window where you can select a data table containing values for the Forest panel tuning parameters, called a tuning design table. A tuning design table has a column for each option that you want to specify and has one or multiple rows that each represent a single Bootstrap Forest model design. If an option is not specified in the tuning design table, the default value is used.

For each row in the table, JMP creates a Bootstrap Forest model using the tuning parameters specified. If more than one model is specified in the tuning design table, the Model Validation-Set Summaries report lists the RSquare value for each model. The Bootstrap Forest report shows the fit statistics for the model with the largest RSquare value.

You can create a tuning design table using the Design of Experiments facilities. A bootstrap forest tuning design table can contain the following case-insensitive columns in any order:

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Number Trees

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Number Terms

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Portion Bootstrap

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Minimum Splits per Tree

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Maximum Splits per Tree