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smotenode properties
smotenode properties
Last updated: Feb 12, 2025
smotenode properties

SMOTE node iconThe Synthetic Minority Over-sampling Technique (SMOTE) node provides an over-sampling algorithm to deal with imbalanced data sets. It provides an advanced method for balancing data. The SMOTE process node in SPSS Modeler is implemented in Python and requires the imbalanced-learn© Python library.

Table 1. smotenode properties
smotenode properties Data type Property description
target field The target field.
sample_ratio string Enables a custom ratio value. The two options are Auto (sample_ratio_auto) or Set ratio (sample_ratio_manual).
sample_ratio_value float The ratio is the number of samples in the minority class over the number of samples in the majority class. It must be larger than 0 and less than or equal to 1. Default is auto.
enable_random_seed Boolean If set to true, the random_seed property will be enabled.
random_seed integer The seed used by the random number generator.
k_neighbours integer The number of nearest neighbors to be used for constructing synthetic samples. Default is 5.
m_neighbours integer The number of nearest neighbors to be used for determining if a minority sample is in danger. This option is only enabled with the SMOTE algorithm types borderline1 and borderline2. Default is 10.
algorithm string The type of SMOTE algorithm: regular, borderline1, or borderline2.
use_partition Boolean If set to true, only training data will be used for model building. Default is true.