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randomtrees properties
Last updated: Feb 12, 2025
randomtrees properties

Random Trees node iconThe Random Trees node is similar to the C&RT Tree node; however, the Random Trees node is designed to process big data to create a single tree. The Random Trees tree node generates a decision tree that you use to predict or classify future observations. The method uses recursive partitioning to split the training records into segments by minimizing the impurity at each step, where a node in the tree is considered pure if 100% of cases in the node fall into a specific category of the target field. Target and input fields can be numeric ranges or categorical (nominal, ordinal, or flags); all splits are binary (only two subgroups).

Table 1. randomtrees properties
randomtrees Properties Values Property description
target field In the Random Trees node, models require a single target and one or more input fields. A frequency field can also be specified. See Common modeling node properties for more information.
number_of_models integer Determines the number of models to build as part of the ensemble modeling.
use_number_of_predictors flag Determines whether number_of_predictors is used.
number_of_predictors integer Specifies the number of predictors to be used when building split models.
use_stop_rule_for_accuracy flag Determines whether model building stops when accuracy can't be improved.
sample_size number Reduce this value to improve performance when processing very large datasets.
handle_imbalanced_data flag If the target of the model is a particular flag outcome, and the ratio of the desired outcome to a non-desired outcome is very small, then the data is imbalanced and the bootstrap sampling that's conducted by the model may affect the model's accuracy. Enable imbalanced data handling so that the model will capture a larger proportion of the desired outcome and generate a stronger model.
use_weighted_sampling flag When False, variables for each node are randomly selected with the same probability. When True, variables are weighted and selected accordingly.
max_node_number integer Maximum number of nodes allowed in individual trees. If the number would be exceeded on the next split, tree growth halts.
max_depth integer Maximum tree depth before growth halts.
min_child_node_size integer Determines the minimum number of records allowed in a child node after the parent node is split. If a child node would contain fewer records than specified here, the parent node won't be split.
use_costs flag  
costs structured Structured property. The format is a list of 3 values: the actual value, the predicted value, and the cost if that prediction is wrong. For example: tree.setPropertyValue("costs", [["drugA", "drugB", 3.0], ["drugX", "drugY", 4.0]])
default_cost_increase none linear square custom Note this is only enabled for ordinal targets. Set default values in the costs matrix.
max_pct_missing integer If the percentage of missing values in any input is greater than the value specified here, the input is excluded. Minimum 0, maximum 100.
exclude_single_cat_pct integer If one category value represents a higher percentage of the records than specified here, the entire field is excluded from model building. Minimum 1, maximum 99.
max_category_number integer If the number of categories in a field exceeds this value, the field is excluded from model building. Minimum 2.
min_field_variation number If the coefficient of variation of a continuous field is smaller than this value, the field is excluded from model building.
num_bins integer Only used if the data is made up of continuous inputs. Set the number of equal frequency bins to be used for the inputs; options are: 2, 4, 5, 10, 20, 25, 50, or 100.
topN integer Specifies the number of rules to report. Default value is 50, with a minimum of 1 and a maximum of 1000.