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Random Classification Forest Options

Use this to select different options to be used in constructing a Random Classification Forest and the output displayed.


Specifies which items of output are to be displayed in the Output window.

Out of bag identifications Out of bag identifications of the groups
Out of bag error Out of bag error (percentage of misclassifications)
Confusion matrix The cross tabulation of the observations by the true groups and the predicted groups
Importance of X-variables The importance of the X-variables in the selected trees in the forest
Ordered importance The importance of the X-variables displayed in decreasing order
Monitoring Monitoring information during the construction process

Random forest generation

The following four settings control how the random forest is generated.

Number of trees in forest

Specifies the number of random trees to form in the forest. Using a larger value may improve the precision, but will take longer to generate.

Number of Xs to select at random

Specifies the number of variables to select randomly from the X-variables list on the main menu for each tree. This must be a positive number less than the number of variables.

Number of units to select at random

Specifies the number of units to select randomly from the observations for each tree. A usual choice would be a value corresponding to between %50 and 90% of the observations, with a typical value being 67%. This must be a positive number less than the number of observations in the variables.

Seed for random number generation

This gives a seed to initialize the random number generation used for the random selections of variates and units. Using zero initializes this from the computer’s clock, but specifying an nonzero value gives a repeatable analysis.

X-variable factor levels ordered

Specifies whether the x-variable factor levels are ordered. Splits are then tried only between adjacent levels.


The effectiveness of a factor or variate to be chosen at each node depends on how the groups are split between remaining subsets. This option lets you choose the method to assess this. You can select either the Gini information criterion or the Mean posterior improvement criterion.

Anti-end-cut factor

Controls whether anti-end-cut factors are used.

See also

Updated on February 8, 2023

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