rfnode properties

Last updated: Jan 18, 2024

rfnode properties

Random Forest node icon The Random Forest node uses an advanced implementation of a bagging algorithm with a tree model as the base model. This Random Forest modeling node in SPSS Modeler is implemented in Python and requires the scikit-learn© Python library.

Table 1. rfnode properties
`rfnode` properties	Data type	Property description
`custom_fields`	boolean	This option tells the node to use field information specified here instead of that given in any upstream Type node(s). After selecting this option, specify the following fields as required.
`inputs`	field	List of the field names for input.
`target`	field	One field name for target.
`fast_build`	boolean	Utilize multiple CPU cores to improve model building.
`role_use`	string	Specify `predefined` to use predefined roles or `custom` to use custom field assignments. Default is predefined.
`splits`	field	List of the field names for split.
`n_estimators`	integer	Number of trees to build. Default is `10`.
`specify_max_depth`	Boolean	Specify custom max depth. If `false`, nodes are expanded until all leaves are pure or until all leaves contain less than `min_samples_split` samples. Default is `false`.
`max_depth`	integer	The maximum depth of the tree. Default is `10`.
`min_samples_leaf`	integer	Minimum leaf node size. Default is `1`.
`max_features`	string	The number of features to consider when looking for the best split: If `auto`, then `max_features=sqrt(n_features)` for classifier and `max_features=sqrt(n_features)` for regression. If `sqrt`, then `max_features=sqrt(n_features)`. If `log2`, then `max_features=log2 (n_features)`. Default is `auto`.
`bootstrap`	Boolean	Use bootstrap samples when building trees. Default is `true`.
`oob_score`	Boolean	Use out-of-bag samples to estimate the generalization accuracy. Default value is `false`.
`extreme`	Boolean	Use extremely randomized trees. Default is `false`.
`use_random_seed`	Boolean	Specify this to get replicated results. Default is `false`.
`random_seed`	integer	The random number seed to use when build trees. Specify any integer.
`cache_size`	float	The size of the kernel cache in MB. Default is `200`.
`enable_random_seed`	Boolean	Enables the `random_seed` parameter. Specify true or false. Default is `false`.
`enable_hpo`	Boolean	Specify `true` or `false` to enable or disable the HPO options. If set to `true`, Rbfopt will be applied to determine the "best" Random Forest model automatically, which reaches the target objective value defined by the user with the following `target_objval` parameter.
`target_objval`	float	The objective function value (error rate of the model on the samples) you want to reach (for example, the value of the unknown optimum). Set this parameter to the appropriate value if the optimum is unknown (for example, `0.01`).
`max_iterations`	integer	Maximum number of iterations for trying the model. Default is `1000`.
`max_evaluations`	integer	Maximum number of function evaluations for trying the model, where the focus is accuracy over speed. Default is `300`.